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    Discussion on Existing Standards and Quality Criteria in Nanosafety Research : Summary of the NanoS-QM Expert Workshop
    (Zenodo, 2021) Binder, Kunigunde; Bonatto Minella, Christian; Elberskirchen, Linda; Kraegeloh, Annette; Liebing, Julia; Petzold, Christiane; Razum, Matthias; Riefler, Norbert; Schins, Roel; Sofranko, Adriana; van Thriel, Christoph; Unfried, Klaus
    The partners of the research project NanoS-QM (Quality- and Description Standards for Nanosafety Research Data) identified and invited relevant experts from research institutions, federal agencies, and industry to evaluate the traceability of the results generated with the existing standards and quality criteria. During the discussion it emerged that numerous studies seem to be of insufficient quality for regulatory purposes or exhibit weaknesses with regard to data completeness. Deficiencies in study design could be avoided by more comprehensive use of appropriate standards, many of which already exist. The use of Electronic Laboratory Notebooks (ELNs) that allow for early collection of metadata and enrichment of datasets could be one solution to enable data re-use and simplify quality control. Generally, earlier provision and curation of data and metadata indicating their quality and completeness (e.g. guidelines, standards, standard operating procedures (SOPs) that were used) would improve their findability, accessibility, interoperability, and reusability (FAIR) in the nanosafety research field.
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    Knowledge Graphs - Working Group Charter (NFDI section-metadata) (1.2)
    (Genève : CERN, 2023) Stocker, Markus; Rossenova, Lozana; Shigapov, Renat; Betancort, Noemi; Dietze, Stefan; Murphy, Bridget; Bölling, Christian; Schubotz, Moritz; Koepler, Oliver
    Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains.