TY - JOUR A1 - Ghiringhelli, Luca M. A1 - Baldauf, Carsten A1 - Bereau, Tristan A1 - Brockhauser, Sandor A1 - Carbogno, Christian A1 - Chamanara, Javad A1 - Cozzini, Stefano A1 - Curtarolo, Stefano A1 - Draxl, Claudia A1 - Dwaraknath, Shyam A1 - Fekete, Ádám A1 - Kermode, James A1 - Koch, Christoph T. A1 - Kühbach, Markus A1 - Ladines, Alvin Noe A1 - Lambrix, Patrick A1 - Himmer, Maja-Olivia A1 - Levchenko, Sergey V. A1 - Oliveira, Micael A1 - Michalchuk, Adam A1 - Miller, Ronald E. A1 - Onat, Berk A1 - Pavone, Pasquale A1 - Pizzi, Giovanni A1 - Regler, Benjamin A1 - Rignanese, Gian-Marco A1 - Schaarschmidt, Jörg A1 - Scheidgen, Markus A1 - Schneidewind, Astrid A1 - Sheveleva, Tatyana A1 - Su, Chuanxun A1 - Usvyat, Denis A1 - Valsson, Omar A1 - Wöll, Christof A1 - Scheffler, Matthias T1 - Shared metadata for data-centric materials science N2 - The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. KW - Library and Information Sciences KW - Statistics, Probability and Uncertainty KW - Computer Science Applications KW - Education KW - Information Systems KW - Statistics and Probability PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584846 DO - https://doi.org/10.1038/s41597-023-02501-8 VL - 10 IS - 1 SP - 1 EP - 18 PB - Springer Science and Business Media LLC AN - OPUS4-58484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schilling, Markus A1 - Bruns, Sebastian A1 - Bayerlein, Bernd A1 - Kryeziu, Jehona A1 - Schaarschmidt, Jörg A1 - Waitelonis, Jörg A1 - Dolabella Portella, Pedro A1 - Durst, Karsten T1 - Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation N2 - The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies. KW - Semantic Data KW - Data Integration KW - Tensile Test Ontology KW - Electronic Lab Notebook KW - Wissensrepräsentation KW - Education PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-618251 DO - https://doi.org/10.1002/adem.202401527 SP - 1 EP - 13 PB - Wiley VHC-Verlag AN - OPUS4-61825 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -