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- ItemWorkshop on PIDs within NFDI: Report of the Working Group “Persistent Identifiers (PID)” of the Section Common Infrastructures of the NFDI(Zenodo, 2023) Arend, Daniel; Bach, Janete; Elger, Kirsten; Göller, Sandra; Hagemann-Wilholt, Stephanie; Krahl, Rolf; Lange, Matthias; Linke, David; Mayer, Desiree; Mutschke, Peter; Reimer, Lorenz; Scheidgen, Markus; Schrader, Antonia C.; Selzer, Michael; Wieder, PhilippIn order to gain an overview of the current state of the discussion on PIDs and for the identification of use cases for the initiation phase of a PID service within the NFDI basic services, the working group Persistent Identifier of the Section Common Infrastructures of the NFDI hosted an online workshop in January 2023. In the course of the workshop, members of nine different NFDI consortia presented the current application of PIDs in their consortia.
- ItemIsn't a number and a URL enough? Why PIDs matter and technical solutions alone are not sufficient(Zenodo, 2023) Schrader, Antonia C.; Hagemann-Wilholt, Stephanie; Czerniak, AndreasIn the presentation, we introduce the two projects PID4NFDI and PID Network Germany that deal with PIDs at the national level, present some initial findings and highlight their benefit for NFDI. PIDs are used and needed along the entire lifecycle of research data: from enabling to connecting. However, a particular focus for the presentation will be laid on harmonising and connecting.
- ItemPID4NFDI: Survey on PID Practices. Main results(Zenodo, 2023) Hagemann-Wilholt, StephanieIn December 2022 and January 2023, the NFDI working group on Persistent Identifier Services conducted a survey among infrastructure managers of NFDI services to learn about current PID integrations and future plans on PID usage. The slides summarise the key results of the survey.
- ItemConcept for Setting up the Persistent Identifier Services Working Group in the NFDI Section "Common Infrastructures"(Zenodo, 2022) Bingert, Sven; Brase, Jan; Burger, Felix; Dreyer, Britta; Hagemann-Wilholt, Stephanie; Vierkant, Paul; Wieder, PhilippThe aim of this NFDI working group is to develop a common strategy for the implementation and extension of PID services that is closely aligned with the needs of NFDI consortia. Resulting solutions should enable FAIR research workflows balancing out generic metadata requirements for PIDs that maximise resource discoverability on the one hand and subject-specific needs on the other. At the technical level, the partners want to realise interoperability between PID types and established systems and build on a high level of maturity here; jointly developed services should be able to be rolled out for the entire NFDI.
- ItemKnowledge 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, OliverKnowledge 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.
- ItemThe Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI(Hannover : TIB Open Publishing, 2023) Rossenova, Lozana; Schubotz, Moritz; Shigapov, RenatThe Strategic Research and Innovation Agenda (SRIA) of the European Commission identifies Knowledge Graphs (KGs) as one of the most important technologies for building an interoperability framework and enabling data exchange among users across countries, sectors, and disciplines [1]. KG is a graph-structured knowledge base containing a terminology (vocabulary or ontology) and data entities interrelated via the terminology [2]. KGs are based on semantic web technologies (RDF, SPARQL, etc.) and often used for agile data integration. KGs also play an essential role within Germany as a vehicle to connect research data and research-related entities and make those accessible – examples include the GESIS Knowledge Graph Infrastructure, TIB Open Research Knowledge Graph, and GND.network. Furthermore, the Wikidata knowledge graph, maintained by Wikimedia Germany, contains a large number of research-related entities and is widely used in scientific knowledge management in addition to being an important advocacy tool for open data [3]. Extending domain-specific ontology-supported KGs with the multidisciplinary, crowdsourced knowledge in Wikidata KG would enable significant applications. The linking between expert knowledge systems and world knowledge empowers lay persons to benefit from high-quality research data and ultimately contributes to increasing confidence in scientific research in society.