TY - CONF A1 - Ávila Calderón, Luis T1 - Research Data Management for Creep Reference Data of Ni Based Superalloys N2 - In times of digital data processing and sharing, a central challenge for the Materials Science and Engineering (MSE) community is the complete digital representation of materials, including their relevant processing and microstructure details and the resulting properties. This digital representation, supported by digital infrastructures, enables entirely new qualities of collaborative work and is central to how we will conduct MSE research and develop products in the future. The main challenges are the composition, shaping, and description of the data so that it can be easily interpreted, trusted, and re-used. This presentation introduces our current effort to develop a best practice framework for generating and distributing creep reference datasets for Ni-based superalloys, which builds on previous approaches. Researchers can use such datasets to compare their own experimental or simulation results, used, for instance, to perform simulation-based alloy design and optimization or in the verification of the own testing equipment. The concept includes building an easily accessible digital infrastructure to annotate and discover the datasets and the integration of an ontology that aligns with the necessary metadata schema. With the help of an ontology, the reference dataset for creep can be semantically described, thus enabling the digital representation and reusage of shared concepts from domain experts and the community. To achieve this, it is necessary to establish a defined vocabulary with corresponding relations and annotations. The interplay between a digital infrastructure, the agreement on the necessary metadata and related metadata schema, and an underlying ontology will ensure that data and results can be easily shared, taken up, and re-used, thus fulfilling the FAIR principles (Findable, Accessible, Interoperable, Reusable). Furthermore, we aim to develop a widely accepted definition of reference data and data quality levels and to reach a self-supported community process. We intend to motivate the discussion about reference material datasets, necessary metadata, and future collaborations within the creep community. T2 - 16th International Conference on Creep and Fracture of Engineering Materials and Structures CY - Bangalore, India DA - 28.07.2024 KW - Reference Data KW - Creep KW - Data schema KW - Research Data Management PY - 2024 AN - OPUS4-60837 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Hickel, Tilmann A1 - Richter, Silke A1 - Bitzek, E. A1 - Ávila Calderón, Luis A1 - Gedsun, A. A1 - Forti, M. A1 - Hammerschmidt, T. A1 - Olbricht, Jürgen A1 - Skrotzki, Birgit T1 - NFDI-MatWerk/IUC02 Definition for Reference Data of Materials N2 - This definition was agreed within the infrastructure use case IUC02 “Framework for curation and distribution of reference datasets” of the consortium NFDI-MatWerk. It is broadly formulated to cover experimental and simulation MSE data and will be developed further in agreement with the MSE community. KW - Referenzdaten KW - Digitalisierung KW - NFDI-MatWerk KW - Research Data Management KW - Reference Data PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-611688 DO - https://doi.org/10.5281/zenodo.11667673 PB - Zenodo CY - Geneva AN - OPUS4-61168 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ávila Calderón, Luis A1 - Shakeel, Y. A1 - Gedsun, A. A1 - Forti, M. A1 - Hunke, S. A1 - Han, Ying A1 - Hammerschmidt, T. A1 - Aversa, R. A1 - Olbricht, Jürgen A1 - Chmielowski, M. A1 - Stotzka, R. A1 - Bitzek, E. A1 - Hickel, Tilmann A1 - Skrotzki, Birgit T1 - Management of reference data in materials science and engineering exemplified for creep data of a single-crystalline Ni-based superalloy N2 - The identification of process-structure-property relationships of materials inevitably requires the combination of research data from different measurements. Therefore, the concepts related to FAIR (findable, accessible, interoperable, reusable) data handling, increasingly reported in literature, are particularly important in the materials science and engineering domain. However, they have not yet been integrated into a single, overarching methodological framework, particularly for reference data. Here, we introduce such a framework. Our concept covers data generation, documentation, handling, storage, sharing, data search and discovery, retrieval, and usage. Furthermore, we prototypically implement it using a real dataset with creep data of a single-crystal CMSX-6 Ni-based superalloy. The presented implementation is traceable and permanently accessible through open repositories. The individual elements considered in the framework ensure the functionality and usability of the data and, thus, the adherence to the FAIR principles. In conjunction with this, we present a definition for reference data of materials. Our definition underlines particularly the importance of a comprehensive documentation, e.g., on material provenance, data processing procedures, and the software and hardware used, including software-specific input parameters, as these details enable data users or independent parties to assess the quality of the datasets and to reuse and reproduce the results. Reference data that is managed according to the proposed framework can be used to advance knowledge in the materials science and engineering domain, e.g., by identifying new process-structure-property relations. KW - Referenzdaten KW - NFDI-MatWerk KW - Data schema KW - Research Data Management KW - Reference Data PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625047 DO - https://doi.org/10.1016/j.actamat.2025.120735 VL - 286 SP - 1 EP - 15 PB - Elsevier Inc. AN - OPUS4-62504 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 - 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 - 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 -