TY - THES A1 - Schilling, Markus T1 - Environmental Stress Cracking (ESC) and Slow Crack Growth (SCG) of PE-HD induced by external fluids N2 - High-density polyethylene (PE-HD) is widely used as a packaging material. Typical applications are pipes and containers for storage and transport of dangerous goods. For these applications, the understanding of the craze-crack damage mechanisms slow crack growth (SCG) and environmental stress cracking (ESC) is of importance. Since these mechanisms are considered to be the major causes of failure, their understanding is essential for inspection and release of those materials. A well-established test method for the assessment of these damage mechanisms is the full-notch creep test (FNCT). It is used in this study for a detailed investigation of crack propagation phenomena in PE-HD container materials under the influence of different fluids such as air, water and aqueous detergent solutions (Arkopal N 100) as well as biodiesel and diesel. Based on the results of the FNCT, a classification scheme of different fluids is proposed, which allows for an assignment of the respective damage mechanisms. Hereby, it is differentiated between (i) inert, (ii) purely surface-active and (iii) additionally sorptive, bulk-active fluids with respect to SCG. If the test fluid changes the intrinsic properties (at the surface or in the bulk), the damage mechanism is addressed to ESC behavior. In FNCT investigations, stress, temperature and specimen geometry were varied systematically. In addition to the time to failure as common measure for the resistance of a PE-HD type against crack propagation, specimen elongation was considered in detail. Several imaging techniques were applied for fracture surface analysis of specimens tested in FNCT to gain novel information on SCG and ESC behavior. From height profiles obtained by laser scanning microscopy (LSM) and information on surface structures from scanning electron microscopy (SEM), indicators for the differentiation of the crack propagation mechanisms could be derived. Based on the LSM data, an algorithm for the distinction between ductile shear deformation and brittle crack growth as dominating failure mechanism was developed. Imaging techniques were also used for determination of crack propagation rates, which were related to time-resolved FNCT elongation data. From the time-resolved determination of crack lengths of partly damaged FNCT specimens, an increasing length of craze zone with a progressively propagating crack was revealed for the first time. This relation of crack and craze zones was specified by fracture mechanical considerations. N2 - Polyethylen hoher Dichte (PE-HD) wird als Werkstoff für Rohre und Behälter für den Transport und zur Lagerung von Gefahrgütern verwendet. Für die Beurteilung und technische Freigabe dieser Materialien ist das Verständnis der beiden Schädigungsmechanismen „langsames Risswachstum“ (engl.: „slow crack growth“, SCG) und „umgebungsbedingter Spannungsriss“ (engl.: „environmental stress cracking“, ESC) essentiell. Eine etablierte Prüfmethode zur Bewertung dieser Schädigungsmechanismen ist der Full-Notch Creep Test (FNCT), der in dieser Arbeit zur systematischen Untersuchung des Risswachstums in PE-HD Behältermaterialien unter Einwirkung von Luft, Wasser und wässrigen Netzmittellösungen (Arkopal N 100) sowie Biodiesel und Diesel verwendet wird. Aus den Ergebnissen des FNCT wird ein Klassifikationsschema für Fluide vorgeschlagen, welches ebenfalls eine Zuordnung zu den Schädigungsmechanismen erlaubt. Hierbei wird in (i) inerte, (ii) rein oberflächen-aktive und (iii) zusätzliche sorptive, volumen-aktive Fluide hinsichtlich des langsamen Risswachstums (SCG) unterschieden. Wenn ein Fluid lokal die intrinsischen Materialeigenschaften des Polymers verändert, wird der Schädigungsmechanismus dem umgebungsbedingten Spannungsriss (ESC) zugeordnet. Bei den FNCT-Untersuchungen wurden die mechanische Spannung, die Temperatur und die Prüfkörpergeometrie systematisch variiert. Zusätzlich zur Standzeit wurde die Prüfkörperdehnung zeitabhängig erfasst. Aus einer erweiterten Bruchflächenanalyse konnten neuartige Informationen über SCG und ESC erhalten werden. Hierzu wurden verschiedene Bildgebungsverfahren verwendet. Insbesondere wurden mit Laserscanningmikroskopie (LSM) Höhenprofile und mit Rasterelektronenmikroskopie (REM) Oberflächeninformationen zur Charakterisierung der Rissfortschrittsmechanismen erhalten. Auf Basis der LSM wurde unter Zuhilfenahme von Höhenprofildaten ein Algorithmus zur Unterscheidung zwischen duktiler Scherverformung und sprödem Risswachstum als dominierende Schädigungsmechanismen entwickelt. Die aus den bildgebenden Verfahren ermittelten Rissfortschrittsraten konnten mit den Daten der während des FNCT erfassten Dehnung der Prüfkörper in Beziehung gesetzt werden. Weiterhin wurde mithilfe von zeitaufgelösten Risslängendaten erstmals eine direkte Korrelation der Risslänge zu vorgeschädigten, fibrillierten Bereichen (Crazes) im PE-HD Prüfkörper während des FNCT nachgewiesen. Demnach vergrößert sich die Craze-Länge linear mit zunehmender Risslänge. Dieser Zusammenhang zwischen Riss- und Craze-Längen wurde auf mathematisch, bruchmechanischer Grundlage bestätigt. KW - High-density polyethylene (PE-HD) KW - Full-Notch Creep Test (FNCT) KW - Slow crack growth (SCG) KW - Environmental Stress Cracking (ESC) KW - Biodiesel KW - Diesel KW - Crack propagation analysis KW - Fracture Surface Analysis KW - Test Improvement KW - Imaging PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:tuda-tuprints-115443 DO - https://doi.org/10.25534/tuprints-00011544 SP - 1 EP - 212 CY - Darmstadt AN - OPUS4-50941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Tensile Test: From Standard to PMD Application Ontology N2 - Data analysis methods play an important role in both the experimental and simulation-based digital description of materials but have so far been poorly structured. The platform Material Digital (PMD) should contribute to a standardized description of data processing methods in materials research. The goal is the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. Therefore, application ontologies are created to explicitly describe processes and test methods. In this presentation, the first efforts in creating a tensile test application ontology in accordance with the ISO standard 6892-1:2019-11 are shown. Especially, the path of ontology development to be pursued based on standards was focused. T2 - Onboarding Workshop der Plattform Material Digital (PMD) CY - Online meeting DA - 13.04.2021 KW - Ontology KW - Platform MaterialDigital KW - PMD KW - Tensile Test KW - Normung KW - Standardization PY - 2021 AN - OPUS4-52425 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Tensile Test Ontology used in Platform Material Digital (PMD) N2 - Data analysis methods play an important role in both the experimental and simulation-based digital description of materials but have so far been poorly structured. The platform Material Digital (PMD) is supposed to contribute to a standardized description of data processing methods in materials research. The goal is the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. Therefore, application ontologies are created to explicitly describe processes and test methods. In this presentation, the first efforts within the joint project PMD in creating a tensile test application ontology in accordance with the ISO standard 6892-1:2019-11 are shown. Especially, the path of ontology development to be pursued based on standards was focused. Furthermore, the presentation includes a live demonstration of queries possibly performed to query data that was uploaded in the PMD triple store. T2 - Online Workshop: An introduction to the semantic web and ontologies CY - Online meeting DA - 23.04.2021 KW - Ontology KW - Tensile Test KW - Platform Material Digital KW - PMD KW - Knowledge Graphs KW - Semantic Web PY - 2021 AN - OPUS4-52949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus T1 - Tensile Test Ontology (TTO) N2 - This is the stable version 2.0.1 of the PMD ontology module of the tensile test (Tensile Test Ontology - TTO) as developed on the basis of the 2019 standard ISO 6892-1: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature. The TTO was developed in the frame of the PMD project. The TTO provides conceptualizations valid for the description of tensile test and corresponding data in accordance with the respective standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of TTO facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of tensile test data is enhanced and data querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated. The TTO class structure forms a comprehensible and semantic layer for unified storage of data generated in a tensile test including the possibility to record data from analysis, re-evaluation and re-use. Furthermore, extensive metadata allows to assess data quality and reproduce experiments. Following the open world assumption, object properties are deliberately low restrictive and sparse. KW - Ontology KW - Tensile Test KW - Digitalization KW - Plattform MaterialDigital KW - Structured Data PY - 2023 UR - https://github.com/MarkusSchilling/application-ontologies/blob/479311832819af695a2c64fa8eb772f2da398061/tensile_test_ontology_TTO/pmd_tto.ttl UR - https://github.com/materialdigital/core-ontology/blob/59f5727b0437ceea5e3d9fcb8fcd0ac211e92cc3/pmd_tto.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57935 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMD Workshop: Use and benefits of the PMDco N2 - This is an interactive workshop of the Plattform MaterialDigital (PMD) on Semantic Web technologies (SWT) where we delve into the world of ontology development and data structuring in the field of materials science and engineering (MSE). This workshop is designed to equip participants with essential skills in categorizing classes within ontologies, with a special emphasis on the PMD Core Ontology (PMDco). Discover the immense value of ontologies for MSE data reproducibility and reuse and harness the power of PMDco to foster your data management practices. In this workshop, you will explore the intricacies of class categorization when developing ontologies in the MSE domain. Through interactive discussions and hands-on exercises, you will learn effective techniques for structuring and organizing ontologies to enhance data retrieval and analysis. You will gain valuable insights into best practices and considerations for class categorization, aiming at optimal data organization and management within your MSE projects. One of the highlights of this workshop is the introduction to PMDco, a mid-level ontology in the field of MSE developed community-driven in the frame of the project PMD. Learn about PMDco, its underlying concepts, and its application in detail. Discover how PMDco provides a comprehensive framework for MSE data representation, enabling seamless integration and interoperability across different systems and platforms. In addition to exploring PMDco, this workshop will illustrate the benefits of data interoperability and SWT with respect to leveraging the full potential of MSE data. T2 - NFDI MatWerk Conference 2023 CY - Siegburg, Germany DA - 27.06.2023 KW - Workshop KW - Ontology KW - Plattform MaterialDigital KW - PMDco PY - 2023 AN - OPUS4-57802 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Plattform MaterialDigital in DIN NA 062-01-42 AA N2 - In dieser Präsentation werden die letzten Entwicklungen in der Plattform MaterialDigital (PMD) im DIN Normenausschuss 062-01-42 AA zur Zug- und Duktilitätsprüfung für Metalle vorgestellt. Dabei werden hauptsächlich die Entwicklungen und Neustrukturierung der PMD Core Ontology (PMDco) sowie die entsprechende Anpassung der Zugversuchsontologie (Tensile Test Ontology - TTO) inklusive des Datenmappings adressiert. Für das Datenmapping wurde ein speziell im Zusammenhang mit der PMD erstellter Zugversuchsdatensatz verwendet, welcher frei verfügbar in einem online Zenodo Repository veröffentlicht ist. Weiterhin wird die Weiterentwicklung der Norm in Verbindung mit ihrer folgenden Revision hinsichtlich der Integration von Aspekten zur Digitalisierung in der Materialwissenschaft und Werkstofftechnik sowie der Verwendung von semantischen Technologien, wie unter anderem ontologischen Repräsentationen von Materialien und Prozessen, diskutiert. T2 - DIN NA 062-01-42 AA Meeting CY - Berlin, Germany DA - 14.06.2023 KW - Ontologie KW - Semantic Web Technologies KW - Normung KW - DIN KW - Plattform MaterialDigital KW - Zugversuch KW - Tensile Test KW - Materialcharakterisierung PY - 2023 AN - OPUS4-57803 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus T1 - Full-Notch Creep Test Ontology (ontoFNCT) N2 - This is the stable version of the full-notch creep test ontology (OntoFNCT) that ontologically represents the full-notch creep test. OntoFNCT has been developed in accordance with the corresponding test standard ISO 16770:2019-09 Plastics - Determination of environmental stress cracking (ESC) of polyethylene - Full-notch creep test (FNCT). The OntoFNCT provides conceptualizations that are supposed to be valid for the description of full-notch creep tests and associated data in accordance with the corresponding test standard. By using OntoFNCT for storing full-notch creep test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which is meant to lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of OntoFNCT facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of full-notch creep test data is enhanced and querying in combination with other aspects and data within the broad field of materials science and engineering (MSE) is facilitated. The class structure of OntoFNCT forms a comprehensible and semantic layer for unified storage of data generated in a full-notch creep test including the possibility to record data from analysis and re-evaluation. Furthermore, extensive metadata allows to assess data quality and reliability. Following the open world assumption, object properties are deliberately low restrictive and sparse. KW - Ontology KW - Full-Notch Creep Test KW - FNCT KW - Knowledge Representation KW - Semantic Web Technologies KW - Data Structures KW - Data Management PY - 2024 UR - https://github.com/MarkusSchilling/ontoFNCT/blob/4abce82852190a5e444d302da077aa7404f433f0/ontoFNCT.ttl UR - https://raw.githubusercontent.com/MarkusSchilling/ontoFNCT/main/ontoFNCT.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-59815 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - FAIR Data in Platform MaterialDigital (PMD) - Ontologies , Semantic Data Integration and Data Exchange N2 - Following the new paradigm of materials development, design, and optimization, digitalization is the main goal in materials sciences and engineering (MSE) 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 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. The project ‘platform MaterialDigital’ (PMD, https://materialdigital.de) aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific support in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. 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 the PMD project towards the digitalization in MSE such as the development of the mid-level PMD core ontology (PMDco, https://github.com/materialdigital/core-ontology). Furthermore, selected results of a PMD partner project use case addressing data and knowledge management from synthesis, production, and characterization of materials are shown. T2 - 1st VMAP User Meeting 2024 CY - Sankt Augustin, Germany DA - 14.02.2024 KW - Ontology KW - Semantic Web Technologies KW - Plattform MaterialDigital KW - Data Interoperability KW - Data Exchange KW - Data Structures PY - 2024 AN - OPUS4-59567 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus A1 - Bayerlein, Bernd A1 - Birkholz, H. A1 - Fliegener, S. A1 - Grundmann, J. A1 - Hanke, T. A1 - von Hartrott, P. A1 - Waitelonis, J. T1 - PMD Core Ontology (PMDco) N2 - The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. PMDco's class structure is both understandable and extensible, making it an efficient tool for organizing MSE knowledge. It serves as a semantic intermediate layer that unifies MSE knowledge representations, enabling data and metadata to be systematically integrated on key terms within the MSE domain. With PMDco, it is possible to seamlessly trace data generation. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the generation, derivation, and attribution of resources. By building on this foundation, PMDco facilitates the integration of data from various sources and the creation of complex workflows. In summary, PMDco is a valuable tool for researchers and practitioners in the MSE domains. It provides a common language for representing and sharing knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless traceability of data generation. Overall, PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain. PMDco at GitHub: https://github.com/materialdigital/core-ontology KW - Ontology KW - Semantic Web technologies KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2023 UR - https://github.com/materialdigital/core-ontology/blob/f2bd420348b276583fad6fa0fb4225f17b893c78/pmd_core.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-59352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Hochwertige, verlässliche und FAIRe Daten erstellen: Von Ontologien und elektronischen Laborbüchern zu Datenmanagement und Wissenstransfer N2 - Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien wird im Zuge der digitalen Transformation in der Materialwissenschaft und Werkstofftechnologie in verschiedenen Projekten adressiert. Dabei stehen die Interoperabilität von nach FAIR-Prinzipien erstellten und veröffentlichten Daten und Anwendungen im Vordergrund. Zur Umsetzung eines komplexen Datenmanagements sowie der Digitalisierung im Bereich der Materialwissenschaften etablieren sich Ontologien zunehmend als belastbares Werkzeug. Sie ermöglichen sowohl menschenlesbare als auch maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen. Im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de) werden Ontologien verschiedener Ebenen entwickelt (verbindende mid-level sowie Domänen-Ontologien). Die PMD-Kernontologie (PMD Core Ontology - PMDco) ist eine Ontologie der mittleren Ebene), die Verbindungen zwischen spezifischeren MSE-Anwendungsontologien und domänenneutralen Konzepten herstellt, die in bereits etablierten Ontologien höherer Ebenen (top-level Ontology) verwendet werden. Sie stellt somit einen umfassenden Satz von durch Konsensbildung in der Gemeinschaft (geteiltes Vokabular) entstanden Bausteinen grundlegender Konzepte aus der Materialwissenschaft und Werkstofftechnik (MSE) dar. Das primäre Ziel des PMDco-Designs ist es, die Interoperabilität zwischen verschiedenen MSE-bezogenen und anderen Ontologien zu ermöglichen. Die PMDco dient als umfassend ausgelegte und erweiterbare semantische Zwischenschicht, die gemeinsame MSE-Konzepte durch semantisches Mapping auf andere Darstellungen vereinheitlicht, was sie zu einem effizienten Werkzeug zur Strukturierung von MSE-Wissen macht. In dieser Präsentation wird die Version 2.0 der PMDco vorgestellt. Weiterhin wird eine auf die PMDco bezogene normenkonforme ontologische Repräsentation zur Speicherung und Weiterverarbeitung von Zugversuchsdaten präsentiert, die in enger Zusammenarbeit mit Standardisierungsgremien erstellt wurde. Dies umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verbindung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden. Auf Basis dieser Zugversuchsontologie wurde im Folgenden unter Verwendung eines elektronischen Laborbuches (electonic lab notebook – ELN) zur Datenaufnahme eine Praktikumsreihe von Universitätsstudierenden digitalisiert. Dadurch wurde eine vollständig digital integrierte Versuchsführung ermöglicht, die auf andere Versuchsreihen und Experimente übertragbar ist und ebenfalls vorgestellt werden soll. Neben einer erleichterten Aufnahme, Analyse und (Wieder-)Verwendbarkeit von Daten wird damit eine Sensibilisierung von Studierenden für Datenstrukturierung sowie semantische Technologien im Sinne der Aus- und Weiterbildung erreicht. T2 - DVM-Tagung Werkstoffprüfung CY - Berlin, Germany DA - 23.11.2023 KW - Ontologie KW - Semantic Web Technologies KW - Plattform MaterialDigital KW - PMDco KW - Zugversuchsontologie KW - Zugversuch KW - Elektronisches Laborbuch (ELN) PY - 2023 AN - OPUS4-59030 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMDco: 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 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 - Kupfer-Symposium CY - Jena, Germany DA - 29.11.2023 KW - Ontology KW - Semantic Web Technologies KW - Plattform MaterialDigital KW - PMDco PY - 2023 AN - OPUS4-59031 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 - GEN A1 - Glauer, M. A1 - Schilling, Markus A1 - Stappel, M. T1 - A practical ontology development guide N2 - Knowledge representation is becoming increasingly important in view of the large amounts of data that are handled in the modern scientific landscape. Many of the domains that have most readily realised this problem and worked on potential solutions have been the domain of biochemistry. And although the developments sought here were not accompanied by philosophers, this process organically led to the development of formal and structured representations of certain domains. While the resulting structures were not the first formal ontologies, they are ones that are still in wide use to this day. These approaches have led to major advances in the organisation, structuring and communication of scientific results. Since then, a variety of other domains have tried to adapt a similar process and develop their own ontologies. However, the development of ontologies from the domain of biochemistry was the result of a years-long process that also involved a large number of errors and course corrections. One of the greatest challenges is also one of the greatest strengths of ontologies: Interoperability with other ontologies. To ensure this interoperability, ontologies must follow certain principles. In the field of biochemistry, the OBO Foundry has established itself, which offers functionalities for a rich network of ontologies from the domain, but at the same time also defines rules. The purpose of this document is to define a similar set of rules for open ontology development, but which addresses a broader domain and at the same time lowers the barrier of entry for new ontology developers. To this end, we will outline a workflow that can be used to build new ontologies more efficiently. This workflow is based not only on our own years of experience in ontology development, but also on the rules of external experts such as the OBO Foundry. KW - Ontology KW - Data integration KW - Ontologie KW - Data interoperability PY - 2024 UR - https://scientific-ontology-network.github.io/ UR - https://github.com/scientific-ontology-network/ontology-development-guide/releases/download/v0.1.0/ontology-guide.pdf SP - 1 EP - 27 PB - GitHub CY - San Francisco ET - Version 0.1 AN - OPUS4-61140 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Interoperabilität und Wiederverwendung von Materialdaten: Der Weg zu Daten- und Wissensrepräsentationen in der PMD N2 - Die Integration und Wiederverwendung von Wissen und Daten aus der Herstellung, Bearbeitung und Charakterisierung von Materialien wird im Zuge der digitalen Transformation in der Materialwissenschaft und Werkstofftechnologie in verschiedenen Projekten behandelt. Dabei liegt der Fokus auf der Interoperabilität von Daten und Anwendungen, die nach den FAIR-Prinzipien erstellt und veröffentlicht werden. Zur Umsetzung eines komplexen Datenmanagements und der Digitalisierung im Bereich der Materialwissenschaften gewinnen Ontologien zunehmend an Bedeutung. Sie ermöglichen sowohl menschenlesbare als auch maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen. Im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de), welches in dieser Präsentation vorgestellt wird, werden Ontologien verschiedener Ebenen entwickelt (verbindende mid-level sowie Domänen-Ontologien). Die PMD-Kernontologie (PMD Core Ontology - PMDco) ist eine Ontologie der mittleren Ebene, die Verbindungen zwischen spezifischeren MSE-Anwendungsontologien und domänenneutralen Konzepten herstellt, die in bereits etablierten Ontologien höherer Ebenen (top-level Ontology) verwendet werden. Sie stellt somit einen umfassenden Satz von durch Konsensbildung in der Gemeinschaft (geteiltes Vokabular) entstanden Bausteinen grundlegender Konzepte aus der Materialwissenschaft und Werkstofftechnik (MSE) dar. In dieser Präsentation wird die PMDco adressiert. Weiterhin wird eine auf die PMDco bezogene normenkonforme ontologische Repräsentation zur Speicherung und Weiterverarbeitung von Zugversuchsdaten – die Tensile Test Ontology (TTO) – präsentiert, die in enger Zusammenarbeit mit dem zugehörigen DIN-Standardisierungsgremium NA 062-01-42 AA erstellt wurde. Dies umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verbindung von Ontologie und Daten. Auf Grundlage der dabei entwickelten Struktur und Vorgehensweise können weitere Materialcharakterisierungsmethoden semantisch beschrieben werden, woraus sich in diesem Zusammenhang Kooperationsmöglichkeiten ergeben. T2 - 20. Sitzung des NA 062-01-43 AA CY - Online meeting DA - 28.05.2024 KW - Semantische Daten KW - Plattform Material Digital KW - Ontologie KW - Normung KW - Wissensrepräsentation PY - 2024 AN - OPUS4-60178 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science: Insights from Platform MaterialDigital (PMD), Tensile Test Ontology (TTO), Electronic Lab Notebooks (ELN) 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 Series CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Semantic Data KW - Plattform Material Digital KW - Digitalization KW - Data Interoperability KW - NIST KW - Tensile Test Ontology KW - Elctronic Lab Notebook PY - 2024 AN - OPUS4-60392 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Advancing Digital Workflows in Materials Science: The Role of PMDco in Data Integration and Semantic Representation N2 - The field of Materials Science and Engineering (MSE) is undergoing a transformative shift towards digitalization, emphasizing the need for structured and interoperable data management. The Platform MaterialDigital Core Ontology (PMDco), now in version 3.0, addresses these challenges by providing a robust mid-level semantic framework. PMDco bridges the gap between abstract high-level ontologies, such as the Basic Formal Ontology (BFO) standardized in ISO/IEC 21838-2, and highly specific domain terminologies to ensure consistency and interoperability across diverse MSE applications. Developed through MSE community-based curation, PMDco facilitates the integration of real-world data from experiments, simulations, and industrial processes. This presentation will explore PMDco's role in enabling advanced digital workflows and its integration into demonstrators within the Platform MaterialDigital (PMD) initiative. Highlighted use cases include the semantic representation of tensile test data in compliance with ISO 6892-1:2019-11, utilizing the corresponding tensile test ontology (TTO) built on PMDco. Through an electronic laboratory notebook (ELN), data from experiments performed by undergraduate students were transformed into machine-actionable knowledge graphs, demonstrating the potential for education and fully digitalized experimental procedures. Additionally, a possible extension of PMDco as a linking point for semantically representing simulation data will be presented, aligning with the focus of VMAP. This includes linking experimental, simulation, and computational datasets to create comprehensive, FAIR-compliant knowledge ecosystems. By showcasing best practices in data acquisition, semantic integration, and knowledge graph generation, this presentation underscores PMDco’s versatility and its critical role in advancing digital MSE workflows. T2 - VMAP User Forum 2025 CY - Sankt Augustin, Germany DA - 18.02.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-62607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science through Semantic Technologies and Knowledge Graphs N2 - The field of materials science is undergoing a transformative shift driven by digitalization. In this respect, semantic and AI technologies are paving the way for advancements in materials development, design, and optimization while leaping towards an Industry 4.0 environment. Addressing the dual challenges of quality assurance and data interoperability, this presentation examines the integration of semantic technologies and knowledge representation methods. By adhering to FAIR principles, this approach enhances data management, storage, and reuse. That way, both machine-actionable and human-understandable data structures crucial for digital research environments are fostered. This presentation focuses on the ‘platform MaterialDigital’ (PMD) initiative, which aims to support efforts from both industrial and academic sectors to solve digitalization challenges and implement sustainable digital solutions. Besides establishing structures to create virtual material data spaces, PMD develops solutions for systematizing and unifying the handling of hierarchical, process-dependent material data. Semantic technologies play a crucial role in digitalization efforts as they enable the storage, processing, and querying of data in a contextualized form. Therefore, the development and prototypical application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted. This includes the design and documentation of graph patterns that may be compiled into rule-based semantic shapes. Its integration into daily lab life is demonstrated through its application to electronic lab notebooks (ELN). This illustrates potentials of standardized protocols and automation-ready solutions for managing diverse experimental data across different sources. Outlining best practices and illustrating the possibilities that semantic technologies bring to modern labs, examples from materials processing and mechanical testing will underscore how knowledge graphs bridge the gap between data and decision-making in materials science, with potential for increased productivity and streamlined workflows across the field. T2 - Materials Week 2025 CY - Frankfurt am Main, Germany DA - 03.04.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-62866 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus A1 - Marschall, Niklas A1 - Niebergall, Ute A1 - Böhning, Martin T1 - Dataset of comprehensive Full-notch creep tests (FNCT) of selected high-density polyethylene (PE-HD) materials N2 - The dataset provided in this repository comprises data obtained from a series of full-notch creep tests (FNCT) performed on selected high-density polyethylene (PE-HD) materials (for further details, see section 1 Materials in this document) in accordance with the corresponding standard ISO 16770. The FNCT is one of the mechanical testing procedures used to characterize polymer materials with respect to their environmental stress cracking (ESC) behavior. It is widely applied for PE-HD materials, that are predominantly used for pipe and container applications. It is based on the determination of the time to failure for a test specimen under constant mechanical load in a well-defined and temperature controlled liquid environment. The test device used here also allows for continuous monitoring of applied force, specimen elongation and temperature. KW - Polyethylene KW - Environmental Stress Cracking KW - Full-notch creep test KW - PE-HD KW - Crack growth PY - 2023 DO - https://doi.org/10.5281/zenodo.10143351 PB - Zenodo CY - Geneva AN - OPUS4-58835 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Plattform MaterialDigital (PMD): Konsortium, Zielsetzung, Core Ontology N2 - Die Materialwissenschaft und Werkstofftechnik (MSE) durchläuft derzeit eine digitale Transformation, die ein strukturiertes und interoperables Datenmanagement erfordert. Die Plattform MaterialDigital (PMD) adressiert diese Herausforderungen mit der PMD Core Ontology (PMDco), die inzwischen in Version 3.0 vorliegt. Diese mittlere Ontologieebene schafft eine Brücke zwischen abstrakten Top-Level-Ontologien wie der ISO/IEC 21838-2 standardisierten Basic Formal Ontology (BFO) und spezifischen domänenspezifischen Vokabularen. Ziel ist die Förderung semantischer Interoperabilität sowie die Nachvollziehbarkeit und Wiederverwendbarkeit von Daten entlang der gesamten Wertschöpfungskette in der MSE. Die Präsentation gibt einen Überblick über die Zielsetzung des PMD-Konsortiums, die Community-getriebene Entwicklung der PMDco sowie ihre Rolle in digitalen Workflows und Demonstratoren. Praxisbeispiele beinhalten die semantische Modellierung von Zugversuchen gemäß ISO 6892-1:2019-11 mithilfe der darauf aufbauenden Zugversuch-Ontologie (TTO) sowie die Umwandlung experimenteller Daten von Studierenden in maschinenlesbare Wissensgraphen über eine elektronische Laborbuch-Pipeline. Zudem wird die Ontologieentwicklung durch NLP-gestützte Ansätze (z. B. für Mikroskopie) sowie die Harmonisierung heterogener Datenquellen (z. B. im Orowan-Demonstrator) vorgestellt. Damit leistet PMDco einen entscheidenden Beitrag zur digitalen Zukunft der Materialforschung. T2 - Abschlusskolloquium LeBeDigital CY - Berlin, Germany DA - 04.06.2025 KW - Semantic Data KW - Data Integration KW - Digitalisierung KW - Digitale Transformation KW - Plattform MaterialDigital PY - 2025 AN - OPUS4-63295 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Semantic Technologies for Digital Transformation in Materials Science: From PMDco to Prototypical Applications N2 - The digital transformation of Materials Science and Engineering (MSE) is accelerating the adoption of structured, interoperable, and FAIR data practices, in particular with respect to an advanced research data management. Semantic technologies play a pivotal role in this transformation, as the enable the integration, contextualization, and automation of diverse data sources across experimental, computational, and simulation domains. Central to these efforts is the Platform MaterialDigital Core Ontology (PMDco), now in version 3.0, which provides a robust mid-level semantic framework tailored for MSE. PMDco bridges abstract high-level ontologies, such as the Basic Formal Ontology (BFO) standardized in ISO/IEC 21838-2, with domain-specific terminologies to ensure consistency and interoperability across applications. This presentation explores the application of PMDco and its integration into workflows within the Platform MaterialDigital (PMD) initiative. Through its deployment in electronic laboratory notebooks (ELNs), PMDco enables semantic representation of experimental data, such as tensile test results compliant with ISO 6892-1:2019-11, transforming them into machine-actionable knowledge graphs. Prototypical implementations demonstrate how semantic technologies enhance laboratory processes, improve data reuse, and streamline documentation which offers opportunities for automation and education. Further extending its versatility, PMDco serves as a linking point for semantically representing simulation data, enabling comprehensive integration of experimental and computational datasets. This creates structured data spaces that support advanced digital workflows. Beyond PMDco, the presentation highlights the design of graph patterns and semantic shapes, showcasing generalizable methods for managing diverse data in MSE being based on data structuring and formatting. By presenting best practices in ontology development, data acquisition, and knowledge graph generation, this talk underscores the transformative potential of semantic technologies in MSE. It offers a forward-looking perspective on the role of structured data spaces as a driver for innovation, ensuring that materials science continues to advance through rigorous, interoperable, and automated digital methodologies. T2 - FEMS Euromat 2025 CY - Granada, Spanien DA - 14.09.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology KW - Graph Patterns PY - 2025 AN - OPUS4-64165 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Data-Driven Materials Science: Reproducibility and Standardization N2 - Advancing development and digitalization in materials science requires to focus on quality assurance, interoperability, and compliance with FAIR principles. Semantic technologies offer effective solutions for these challenges by enabling the storage, processing, and contextualization of data in machine-actionable and human-readable formats – essential for robust data management. This presentation highlights the PMD Core Ontology 3.0 (PMDco 3.0), developed specifically for the field of materials science and engineering, and its implementation within generic knowledge representation frameworks. Demonstrators such as standardized mechanical testing, material processing workflows, and the Orowan Demonstrator exemplify the ontology’s practical applications. The use of graph patterns, able to be compiled into rule-based semantic shapes, supports a unified and automated approach to managing heterogeneous experimental data across domains. T2 - Persson Group Seminar CY - Berkeley, CA, USA DA - 23.06.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology KW - Graph Patterns PY - 2025 AN - OPUS4-63484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - On Shared Vocabulary, Ontologies, Semantic Data and Prototype Applications N2 - The advancement of development and digitalization in materials science necessitates rigorous quality assurance, interoperability, and adherence to FAIR principles. Semantic technologies contribute to these objectives by facilitating the structured storage, processing, and contextualization of data, yielding machine-actionable and human-interpretable knowledge representations vital for modern data management. This presentation showcases the prototypical application of generic approaches of knowledge representation in materials science. It includes the design and documentation of graph patterns that may be compiled into rule-based semantic shapes. The development and application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted. Its integration into daily lab life is demonstrated through its functional incorporation into electronic lab notebooks (ELN). In this respect, a possible integration of semantic technologies into openBIS is presented. The openBIS system is usable as a central data storage system that may be complimented by semantic annotation of data to enhance data contextualization and automation. Graph-based knowledge representations and rule-based semantic shapes are shown which were developed alongside the PMD Core Ontology 3.0 (PMDco 3.0) and can enrich openBIS functionalities. Practical examples from material processing and mechanical testing illustrate how semantic extensions of openBIS enable machine-actionable, interoperable, and reusable research data, paving the way for a unified, ontology-driven laboratory data ecosystem. T2 - openBIS User Group Meeting (openBIS UGM) CY - Berlin, Germany DA - 22.09.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - OpenBIS KW - Ontologies KW - Graph Patterns PY - 2025 AN - OPUS4-64307 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Vom Experiment zur semantischen Wissensrepräsentation: Datenstandards und Interoperabilität in der Materialcharakterisierung N2 - Im Zeitalter der digitalen Transformation rückt die strukturierte Erfassung und semantische Verknüpfung von Materialcharakterisierungsdaten immer stärker in den Fokus, um eine effiziente Nutzung über Disziplin- und Projektgrenzen hinweg zu gewährleisten. Dabei bilden etablierte Datenstandards und semantische Technologien sowie Ontologien die Grundlage für eine nach FAIR-Kriterien aufgebaute Dateninfrastruktur, die sowohl maschinenlesbare als auch nachvollziehbare Wissensrepräsentationen ermöglicht. In dieser Präsentation sollen Prinzipien interoperabler Datenarchitekturen erläutert werden, wobei auf die Bedeutung normkonformer Modelle und semantischer Konzeptualisierungen eingegangen wird. Dabei wird der steigende Bedarf an verlässlichen, reproduzierbaren und wiederverwendbaren Daten im Bereich der Materialwissenschaft und Werkstofftechnik adressiert, der insbesondere die Materialcharakterisierung und Werkstoffprüfung vor neue Herausforderungen stellt. Hinsichtlich der angestrebten Möglichkeiten zum erleichterten Datenaustausch kommen einheitlichen Datenformaten und -beschreibungen eine besondere Bedeutung zu. Anhand ausgewählter Publikationen und Demonstratoren wird aufgezeigt, wie Ontologien als verbindende Zwischenschicht unterschiedliche Material- und Verfahrensdomänen konsistent abbilden und zusammenführen können. Ein praktischer Anwendungsfall verdeutlicht die RDF-basierte Repräsentation von Zugversuchsdaten und deren Einbettung in ein Triple-Store-Datenbank-Umfeld. Hierbei fließen Erfahrungen aus der Entwicklung und Anwendung der PMD Core Ontology (PMDco) sowie normenkonformer Ontologien (z.B. Tensile Test Ontology, TTO) ein, welche im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de) erstellt und betrachtet wurden. Darüber hinaus werden weitere methodische Ansätze und Entwicklungen aus diesem Projekt illustriert. T2 - Tagung Werkstoffprüfung 2025 CY - Dresden, Germany DA - 27.11.2025 KW - Wissensrepräsentation KW - Ontologie KW - Werkstoffprüfung KW - Digitale Transformation KW - Interoperabilität PY - 2025 AN - OPUS4-64944 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Towards Structured Data Spaces: Prototypical Application of Semantic Technologies as a Driver for Innovation in Materials Science N2 - In the pursuit of advancing development and digitalization within materials science, ensuring quality assurance, interoperability, and adherence to FAIR principles is significant. To address these aspects, semantic technologies are employed for storage, processing, and contextualization of data, offering machine-actionable and human-readable knowledge representations crucial for data management. This presentation showcases the prototypical application of generic approaches of knowledge representation in materials science. It includes the design and documentation of graph patterns that may be compiled into rule-based semantic shapes. The development and application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted. Its integration into daily lab life is demonstrated through its functional incorporation into electronic lab notebooks (ELN). Examples of material processing and standardized mechanical testing illustrate how knowledge graph operations enhance ELN capabilities, providing a generalizable unified approach for managing diverse experimental data from different sources with automation potentials. T2 - TMS Specialty Congress 2025 CY - Anaheim, CA, USA DA - 15.06.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - Plattform MaterialDigital PY - 2025 AN - OPUS4-63401 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Ansätze zur digitalen Wissensrepräsentation aus der Plattform MaterialDigital (PMD) N2 - Die Digitalisierung von Materialien und Prozessen stellt eine große Herausforderung dar, die nur durch eine Bündelung der Bemühungen aller Beteiligten in diesem Bereich erreicht werden kann. Bei einer derartigen digitalen Beschreibung spielen Datenanalysemethoden, eine Qualitätssicherung von Prozessen inklusive Input- und Output-Daten sowie die Interoperabilität zwischen Anwendungen nach den FAIR-Prinzipien eine wichtige Rolle. Dies umfasst das Speichern, Verarbeiten und Abfragen von Daten in einer vorzugsweise standardisierten Form (Beteiligung von Normungsgremien). Zur Bewältigung dieser Herausforderung ist eine mit allen Stakeholdern konsistente Kontextualisierung der Materialdaten anzustreben, d.h. alle erforderlichen Informationen über den Zustand des Materials einschließlich produktions- und anwendungsbezogener Änderungen müssen über eine einheitliche, maschinenlesbare Beschreibung verfügbar gemacht werden. Dazu werden Wissensrepräsentationen und Konzeptualisierungen ermöglichende Ontologien verwendet. Eine zentrale Betrachtungsweise in diesem Zusammenhang ist die Realisierung von (automatisierten) Datenpipelines, die eine Beschreibung und Verfolgung von Daten ausgehend von ihrer Erzeugung, bspw. in einem Messgerät, bis zu ihrer globalen Verwendung in möglicherweise verschiedenen Kontexten beinhalten. Erste Bemühungen und Ansätze zu diesen Problemstellungen führten im Projekt Innovations-Plattform Material Digital (PMD, materialdigital.de) zur Erstellung von Anwendungsontologien, die Prozesse und Testmethoden explizit beschreiben. Dabei wurde u.a. der Zugversuch an Metallen bei Raumtemperatur nach ISO 6892-1 ontologisch beschrieben. Diese als Beispiel dienende Ontologieentwicklung wird in dieser Präsentation vorgestellt. Weiterhin wurde, ausgehend von der domänenspezifischen Entwicklung von Anwendungsontologien, eine Kernontologie erstellt, die eine übergeordnete Verbindung von ontologischen Konzepten aufgrund der Verwendung gleichen Vokabulars und semantischer Verknüpfungen erlaubt. Diese sowie die das PMD-Projekt selbst werden ebenfalls in dieser Präsentation vorgestellt. T2 - DVM Workshop: Grundlagen und Beispiele zur Digitalisierung für die Materialforschung und -prüfung CY - Online meeting DA - 19.10.2021 KW - Plattform Material Digital (PMD) KW - Ontologie KW - Zugversuch KW - Wissensrepräsentation KW - Semantic Web PY - 2021 AN - OPUS4-53565 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Towards digitalization of materials in PMD: An application ontology of the tensile test N2 - Due to the diversity of materials and the processes associated with their production and use, the complexity of the lifecycles of materials and the multitude of academic and industrial researchers participating in generation of data for material design impose a huge challenge. The topical goal of digitalizing materials and processes can only be adequately addressed by consolidating the efforts of all stakeholders in this field. There are many scattered activities, but there is a demand for an elimination of redundancies as well as an advance in acceptance and a common basis in the digitalization of materials. Furthermore, data analysis methods play an important role in both, the experimental and simulation-based digital description of materials, but they have been poorly structured so far. Therefore, the two joint projects Platform Material Digital (PMD, materialdigital.de) and Materials open Laboratory (Mat-o-Lab, matolab.de) aim to contribute to a standardized description of data processing methods in materials research. Besides stimulating the formation of a collaborative community in this respect, their main technical goals are the quality assurance of the processes and the output data, the acquisition and definition of their accuracy as well as the interoperability between applications. In this regard, data management in accordance with the FAIR (findability, accessibility, interoperability, reuseability) principles is addressed. There is a common agreement in the scientific community following current discussions that data is supposed to be conform to these principles. This includes storage, processing and querying of data in a preferably standardized form. To meet the challenge to contextualize material data in a way that is consistent with all stakeholders, all necessary information on the condition of the material including production and application-related changes have to be made available via a uniform, machine-readable description. For this purpose, ontologies are to be used since they allow for machine-understandable knowledge representations and conceptualizations that are needed for data management and the digitalization in the field of materials science. As first efforts in PMD and Mat-o-Lab, application ontologies are created to explicitly describe processes and test methods. Thereby, the well-known tensile test of metals at room temperature was described ontologically in accordance with the respective ISO standard 6892-1:2019-11. The efforts in creating this tensile test application ontology are shown in this presentation. Especially, the path of ontology development based on standards to be pursued is focused, which is in accordance with the generic recommendations for ontology development and which is supposed to be exemplary for the creation of other application ontologies. T2 - VirtMet: 1st International Workshop on Metrology for Virtual Measuring Instruments and Digital Twins CY - Online meeting DA - 21.09.2021 KW - Platform Material Digital (PMD) KW - Ontology KW - Tensile test KW - Standard KW - Ontology development PY - 2021 AN - OPUS4-53481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Zugversuchsdaten FAIR integriert: Von der normenkonformen Ontologie bis zu interoperablen Daten im Triple Store N2 - Mit der Digitalisierung von Materialien und Prozessen ist ein Paradigmenwechsel in der Materialentwicklung, -gestaltung und -optimierung verbunden, welcher gleichermaßen vielseitige Möglichkeiten eröffnet und eine große Herausforderung darstellt. Insbesondere sind bei den Digitalisierungsbestrebungen die Qualitätssicherung von Prozessen und Ausgabedaten sowie die Interoperabilität zwischen Anwendungen nach FAIR-Prinzipien (Findability, Accessibility, Interoperability, Reusability) sicherzustellen. Dies umfasst die Speicherung, Verarbeitung und Abfrage von Daten in möglichst standardisierter Form, wobei entsprechend auch Normungs- und Standardisierungsgremien beteiligt werden müssen. Um der Herausforderung gerecht zu werden, Materialdaten für alle Beteiligten konsistent zu kontextualisieren, müssen alle notwendigen Informationen zum Zustand des Materials inklusive produktions- und anwendungsbedingter Veränderungen über eine einheitliche, maschinenlesbare Beschreibung verfügbar gemacht werden. Hierfür sollen Ontologien genutzt werden, da sie maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen ermöglichen, die für das Datenmanagement und die Digitalisierung im Bereich der Materialwissenschaften benötigt werden. Dieses hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien wird in den Projekten Innovationsplattform MaterialDigital (PMD, materialdigital.de) und Materials-open-Lab (Mat-o-Lab, matolab.org) adressiert. Diese beiden unter der Beteiligung der Bundeanstalt für Materialforschung und -prüfung (BAM) durchgeführten Projekte wurden auf der 39. Vortrags- und Diskussionstagung „Werkstoffprüfung“ (2021) grundlegend vorgestellt. In dieser Präsentation sollen die Weiterentwicklungen hinsichtlich der Speicherung von Zugversuchsdaten gemäß einer normenkonformen ontologischen Repräsentation vorgestellt werden. Das umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verknüpfung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden. Die übliche Standardisierung von mechanischen Prüfverfahren im Bereich der Materialwissenschaft und Werkstofftechnik dient als solide Grundlage für die Ontologieentwicklung. Daher wurde der Zugversuch von Metallen bei Raumtemperatur nach DIN EN ISO Norm 6892-1:2019-11 als einer der ersten Anwendungsfälle in den genannten Projekten ausgewählt. Die Betrachtung und Beschreibung des Zugversuchs beinhaltet sowohl dessen ontologische Darstellung als auch eine exemplarische Datengenerierung. Die semantische Verbindung von Ontologie und Daten führt zu Interoperabilität und einer verbesserten Abfragefähigkeit. T2 - Tagung Werkstoffprüfung 2022 CY - Dresden, Germany DA - 27.10.2022 KW - Ontologie KW - Semantisches Web KW - Wissensrepräsentation KW - Digitalisierung KW - Zugversuch PY - 2022 AN - OPUS4-56130 LA - deu 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 - Seamless Science in Platform MaterialDigital (PMD): Demonstration of Semantic Data Integration as Good Practices N2 - Following the new paradigm of materials development, design, and optimization, digitalization is the main goal in materials sciences and engineering (MSE) 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 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. The project ‘platform MaterialDigital’ (PMD) aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific support in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are semantically represented on a prototypical basis 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 poster presentation illustrates demonstrators developed and deployed within the PMD project. Semantically anchored using the mid-level PMD Core Ontology (PMDco), they address data transformation leading to a novel data management which is based on semantic integrated data. The PMD data acquisition pipeline (DAP), which is fueled by traditional, diverse data formats, and a pipeline applying an electronic laboratory notebook (ELN) as data source are displayed. Additionally, the efficient combination of diverse datasets originating from different sources is demonstrated by the representation of a use case dealing with the well-known Orowan relation. T2 - Materials Science and Engineering Congress 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Demonstrators PY - 2024 AN - OPUS4-61136 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 - Schilling, Markus T1 - Semantic Technologies in Action: Integrating Mechanical and Microstructure Data in MSE N2 - Semantic technologies (ST) are a powerful tool for storing, processing, and querying data in a contextualized and interoperable manner. They enable machine-actionable and human-readable knowledge representations essential for advanced data management, retrieval, and reuse. As one of the key factors within the frame of the collaborative project platform MaterialDigital (PMD), the establishment of a virtual material data space and the semantic modeling of hierarchical, process-dependent material data is aimed at to serve as best-practice examples of knowledge representation through ontologies and knowledge graphs. In this presentation, the application of ST to a specific use case from the field of materials sciences and engineering (MSE) is demonstrated: the integration and analysis of data related to a 2000 series age-hardenable aluminum alloy. By semantically representing mechanical and microstructural data obtained from tensile tests and dark-field transmission electron microscopy across various aging times, an expandable knowledge graph was constructed that is aligned with the PMD Core Ontology (PMDco) and enriched through the Tensile Test (TTO) and Precipitate Geometry Ontologies. This semantically integrated dataset enables advanced analytical capabilities via SPARQL queries and reveals microstructure–property relationships consistent with the well-known Orowan mechanism. The approach highlights the potential of semantic data integration to support FAIR data principles and to foster a more data-centric and interoperable research infrastructure in MSE. T2 - MSE Research Data Forum 2025 CY - Siegburg, Germany DA - 08.07.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability PY - 2025 AN - OPUS4-63666 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Platform MaterialDigital (PMD) - Knowledge Representation, Interoperability, Reliability N2 - Through collaboration in the materials science and engineering (MSE) community, the PMD is collectively developing common standards and prototype solutions for data acquisition, structuring, storage, and processing. This comprises the development of architectural, standardized and foundational ontologies and workflows. Demonstrators resulting from these developments provide good practice examples. The PMD Core Ontology (PMDco) was created as an anchor that allows implementation of semantic data integration. It provides a common and expanding vocabulary to represent and share knowledge while enabling efficient collaboration and promoting interoperability between diverse domains. T2 - Kupfer-Symposium 2025 CY - Schwäbisch-Gmünd, Germany DA - 12.11.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-64708 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Seamless Science with the Platform MaterialDigital (PMD): Demonstration of Semantic Data Integration as Good Practices N2 - Following the new paradigm of materials development, design, and optimization, digitalization is the main goal in materials sciences and engineering (MSE) 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 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. The project ‘platform MaterialDigital’ (PMD) aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific support in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are semantically represented on a prototypical basis 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 poster presentation illustrates demonstrators developed and deployed within the PMD project. Semantically anchored using the mid-level PMD Core Ontology (PMDco), they address data transformation leading to a novel data management which is based on semantic integrated data. The PMD data acquisition pipeline (DAP), which is fueled by traditional, diverse data formats, and a pipeline applying an electronic laboratory notebook (ELN) as data source are displayed. Additionally, the efficient combination of diverse datasets originating from different sources is demonstrated by the representation of a use case dealing with the well-known Orowan relation. T2 - 9. Dresdner Werkstoffsymposium CY - Dresden, Germany DA - 16.05.2024 KW - Semantic Data KW - Data Integration KW - Plattform MaterialDigital KW - Demonstrators KW - Electronic Lab Notebook PY - 2024 AN - OPUS4-60102 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science: PMD Core Ontology (PMDco) 3.0 – Patterns & Applications N2 - The digitalization of Materials Science and Engineering (MSE) demands standardized, interoperable approaches for representing complex experimental and simulation data. To address this challenge, the Platform MaterialDigital Core Ontology (PMDco) provides a mid-level semantic framework that bridges high-level ontologies and domain-specific terminologies. PMDco version 3.0, recently released, introduces significant enhancements based on a full alignment with the Basic Formal Ontology (BFO) as standardized in ISO/IEC 21838-2. This alignment ensures conceptual consistency and facilitates integration across heterogeneous data sources while enabling FAIR-compliant workflows. First addressing the idea behind a mid-level ontology in MSE, this presentation emphasizes its role in harmonizing diverse data models and supporting machine-actionable knowledge representation. New features and design patterns introduced in PMDco 3.0 will be highlighted, which strengthen interoperability and provide reusable modeling structures for common MSE concepts. Practical applications and prototype implementations will be discussed. PMDco has been developed through active community involvement and its future evolution relies on continued collaboration and discussion within the MSE community. Participation is strongly encouraged to ensure that PMDco remains relevant, comprehensive, and widely adopted. More information and opportunities to contribute can be found at materialdigital.de and github.com/materialdigital/core-ontology. T2 - VMAP User Forum 2026 CY - Sankt Augustin, Germany DA - 24.02.2026 KW - Semantic Technology KW - Knowledge Graph KW - PMD Core Ontology KW - Semantic Pattern KW - Machine-actionability PY - 2026 AN - OPUS4-65574 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus A1 - Skrotzki, Birgit T1 - Full dataset of several mechanical tests on an S355 steel sheet as reference data for digital representations N2 - The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI. The data were generated in the frame of the digitization project Innovationplatform MaterialDigital (PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge. KW - S355 steel sheet KW - Mechanical testing KW - Tensile test KW - Charpy test KW - Microstructure analysis KW - Digital representation KW - Data management KW - Data format PY - 2022 DO - https://doi.org/10.5281/zenodo.6778336 PB - Zenodo CY - Geneva AN - OPUS4-55141 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - FAIR tensile test data in PMD: From a standard-compliant application ontology to RDF data in a triple store N2 - Following the new paradigm of materials development, design and optimization, the digitalization of materials and processes is the main goal 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. This includes storage, processing and querying of data in a preferably standardized form, also addressing the incorporation of standardization bodies. To meet the challenge to contextualize material data in a way that is consistent with all stakeholders, all necessary information on the condition of the material including production and application-related changes have to be made available via a uniform, machine-readable description. For this purpose, ontologies are to be used since they allow for machine-understandable knowledge representations and semantic conceptualizations that are needed for data management and the digitalization in the field of materials science. With respect to this currently ever-growing topic of integration and reuse of data and knowledge from synthesis, production and characterization of materials, this presentation shows the efforts taken within the project Platform MaterialDigital (PMD, materialdigital.de) to store tensile test data in accordance with a standard-compliant ontological representation. The includes the path from developing an ontology in accordance with the respective standard, converting ordinary and arbitrarily selected data gained from standard tests into the interoperable RDF format, up to connecting the ontology and data, respectively. Finally, such data can be queried from a triple store. In the field of material science and engineering (MSE), most mechanical test methods are standardized which serves as a valid basis for ontology development. Therefore, the well-known tensile test of metals at room temperature (DIN EN ISO standard 6892-1:2019-11) was selected to be considered as one of the first use cases in PMD. This consideration within the PMD features both, the ontological representation of such a tensile test in accordance with the standard as well as exemplary data generation. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - S355 steel sheet KW - Mechanical testing KW - Tensile test KW - Digitization KW - Ontology KW - Data structure KW - Material digital PY - 2022 AN - OPUS4-55882 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Transforming Materials Science with ontologies, ELN, and LIMS: Semantic Web Solutions for Digitalization and Data Excellence 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-understandable and human-readable knowledge representations needed for data management, retrieval, and (re)use. The project ‘platform MaterialDigital’ (PMD) is part of an initiative that aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific added values in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are ontologically 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 the PMD project to store data in accordance with a testing standard compliant ontological representation of a tensile test of metals at room temperature (ISO 6892-1:2019-11). This includes the path from developing an ontology in accordance with the respective standard 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 enhanced reusability of data and knowledge from synthesis, production, and characterization of materials, the PMD core ontology (PMDco) was developed as mid-level ontology in the field of MSE. The semantic connection of the tensile test ontology (TTO) to the PMDco leads to enhanced expressivity and interoperability. Moreover, as a best practice example, generation and acquisition of test data semantically connected to the ontology (data mapping) was realized by applying an electronic laboratory notebook (ELN). Corresponding tensile tests were performed by materials science students at university. This enabled a fully digitally integrated experimental procedure that can 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 the sense of education and training. The entire data pipeline is further seamlessly integrable in a laboratory information management system (LIMS). More specifically, the integration of semantic conceptualization and knowledge graphs may become essential parts in LIMS as this would be very beneficial. Therefore, some first approaches of SWT integration in LIMS will also be presented briefly. T2 - TMS Specialty Congress 2024 CY - Cleveland, OH, USA DA - 16.06.2024 KW - Semantic Data KW - Plattform Material Digital KW - Tensile Test Ontology KW - Electronic Lab Notebook KW - Material Life Cycle PY - 2024 AN - OPUS4-60394 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus A1 - Marschall, Niklas A1 - Bayerlein, Bernd A1 - Chen, Yue A1 - Olbricht, Jürgen A1 - Skrotzki, Birgit A1 - von Hartrott, P. A1 - Portella, Pedro Dolabella A1 - Waitelonis, J. A1 - Birkholz, H. A1 - Grundmann, J. ED - Zimmermann, M. T1 - Zugversuchsdaten FAIR integriert: Von einer normenkonformen Ontologie bis zu interoperablen Daten im Triple Store N2 - Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien ('Digitalisierung von Materialien') wird in den Projekten Innovationsplattform MaterialDigital (PMD, materialdigital.de) und Materials-open-Lab (Mat-o-Lab, matolab.org) adressiert. In diesem Beitrag werden die Weiterentwicklungen in diesen Projekten hinsichtlich der Speicherung von Zugversuchsdaten gemäß einer normenkonformen (DIN EN ISO 6892-1:2019-11) ontologischen Repräsentation vorgestellt. Das umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verknüpfung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden. T2 - Werkstoffprüfung 2022 CY - Dresden, Germany DA - 27.10.2022 KW - Ontology KW - Semantic Web KW - Digitalization KW - Knowledge Representation KW - Tensile Test PY - 2022 UR - https://dgm.de/fileadmin/DGM/Veranstaltungen/2022/Werkstoffpruefung/Tagungsband/WP2022-Tagungsband-online.pdf SN - 978-3-88355-430-3 SP - 105 EP - 110 PB - DGM - Deutsche Gesellschaft für Materialkunde e.V CY - Sankt Augustin AN - OPUS4-56836 LA - deu 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 -