Knowledge Graphs (KG) play an integral role in achieving interoperability in NFDI. While several consortia are building individual KG solutions, reusable KG infrastructure-as-a-service (KGI) is missing. To start discipline-specific KG work easily, NFDI consortia, participant institutions and researchers need reusable, scalable KGI. The proposed base service will provide it. Following a landscape analysis, the initialization phase will focus on a pilot KGI based on Wikibase, the software behind Wikidata. Wikidata is among the most popular, large-scale implementations of KG technology worldwide. It is widely used in scientific knowledge management and is an important advocacy tool for open data. Developing the pilot KGI includes establishing easy-to-scale infrastructure-as-a-service allowing NFDI stakeholders to create KGs without administrative overhead; developing an interoperability framework for connecting KGs with research infrastructures; establishing a KGI-consultancy to increase adoption of the KGI-service. The outcomes of the initialization phase ensure the expansion of the service in subsequent phases: the landscape analysis will lead to new partnerships with additional KG-tool providers; the consultancy service will expand operation beyond Wikibase KGs; and the interoperability framework will extend across Wikibase and non-Wikibase KGs towards a unified NFDI with a EOSC compatibility layer.
Was sind Normdaten?Das Tutorial erklärt grundsätzlich, was Normdaten sind und wie sie im Bezug auf Forschungsdaten genutzt werden können. Zudem werden Normdaten für verschiedene Entitäten und von verschiedenen Anbietern thematisiert und ihr Potential für das Semantic Web erläutert.
NFDI4Ing Community Meeting CC-42 2022: Überblick zu NFDI & NFDI4IngEine kurze Einführung in die Nationale Forschungsdateninfrastruktur (NFDI)und die Nationale Forschungsdateninfrastruktur für Ingenieure (NFDI4Ing). A short introduction to the (German) National Research Data Infrastructure (NFDI) and the National Research Data Infrastructure for Engineers (NFDI4Ing).
NFDI ToolTalk: One roof, several services: Bringing together all DataPLANT effortsDescription
Slides of the presentation given on June 08, 2022 as part of the NFDI ToolTalk series. The presentation follows up on the status reached after one and a half year into DataPLANT services discussions and developments. It will show the general concept and the already implemented services and how they could fit into either local or centralized research infrastructures. In the future these services may run on-top of future NFDI base services or integrate NFDI base service components like AAI. DataPLANT services might as well be offered to a wider community beyond fundamental plant research.
FAIRmat, Guide to Writing a Research Data Management PlanIn this guide, we will provide you with comprehensive information and practical tips specific to the fields of condensed-matter physics and materials science on creating a data management plan (DMP) that meets the DFG requirements and aligns your research with the FAIR data principles, the DFG code of conduct, and the EU open science policy. We will take you through the essential components of a DMP, provide tips on data management best practices, and offer guidance on appropriate tools and technologies. By the end of this guide, you will have the knowledge and tools necessary to create a thorough and effective DMP that not only meets the requirements of the DFG but also supports the long-term success of your research project. This guide follows the sections in the DFG checklist, and the questions to be addressed as required by the DFG are listed after each section. Although this guide is based on the requirements of the DFG, the information is not exclusive to any specific funding agency and can be used as a general guide for other research areas. Before preparing a DMP for your project, make sure to check the specific requirements for your funding agency, discipline, and research institution.
DAPHNE4NFDI. DAta from PHoton and Neutron ExperimentsDAPHNE4NFDI is the first (inter-)national attempt to bring together users and large-scale research facilities to create a comprehensive infrastructure to process research DAta from PHoton and Neutron Experiments (DAPHNE) according to the FAIR principles. Our community faces a common need for high-level, rapid data analysis and the challenge of implementing research data management for increasingly large and complex datasets. All this involves not only a broad range of scientific disciplines and stakeholders, but also the connection to complex instrumentation and IT. Within the talk, Lisa Amelung would like to inform about the DAPHNE4NFDI consortium and discuss (current) challenges and future tasks.