2021, Wulf, Christoph, Beller, Matthias, Boenisch, Thomas, Deutschmann, Olaf, Hanf, Schirin, Kockmann, Norbert, Kraehnert, Ralph, Oezaslan, Mehtap, Palkovits, Stefan, Schimmler, Sonja, Schunk, Stephan A., Wagemann, Kurt, Linke, David

Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high-quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level. © 2021 The Authors. ChemCatChem published by Wiley-VCH GmbH

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, Philipp

In 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.

2023, Schrader, Antonia C., Hagemann-Wilholt, Stephanie, Czerniak, Andreas

In 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.

2022, Bingert, Sven, Brase, Jan, Burger, Felix, Dreyer, Britta, Hagemann-Wilholt, Stephanie, Vierkant, Paul, Wieder, Philipp

The 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.

2022, Dellmann, Sarah

Eine Vielzahl von Initiativen und Förderprogrammen zielt darauf, die Etablierung von Forschungsdatenmanagement (FDM) an deutschen Wissenschaftseinrichtungen voranzutreiben. Die Initiativen und Programme unterscheiden sich in Bezug auf Zielgruppe und gewählte Implementierungsstrategie. Welche hochschulinternen und -übergreifenden Faktoren sind für die gelungene Implementierung von FDM-Angeboten vor Ort ausschlaggebend? Der vorliegende Beitrag präsentiert Ergebnisse einer Interviewbefragung unter FDM-Mitarbeiter*innen an hessischen Hochschulen (Universitäten und staatliche Hochschulen für Angewandte Wissenschaften) im November 2020, die mittels qualitativer Inhaltsanalyse ausgewertet wurden. Neben Auflagen von Forschungsförderern sowie der Möglichkeit, sich über FDM hochschulpolitisch zu positionieren, wurden die Verstetigung von Stellen, Engagement der Hochschulleitung und gute Kommunikation der beteiligten zentralen Einrichtungen untereinander, insbesondere mit der Drittmittelstelle, als relevant genannt. Eine besondere Bedeutung bei der Schaffung von FDM-Services maßen die Interviewteilnehmer*innen der Hessischen Forschungsdateninfrastruktur HeFDI bei.

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.

2021, Blümel, Ina, Rossenova, Lozana, Sohmen, Lucia, Vock, Richard, Schubert, Zoe

A suite of tools for semantic annotation of 3D cultural artefacts is being developed as part of the NFDI4Culture project across several partner organisations (led by the Open Science lab at TIB, Hannover). Operating within Task area 1: Data capture and enrichment, the proposed toolchain focuses on the annotation of 3D data within a knowledge graph environment, so that 3D objects’ geometry, attendant metadata, as well as annotations remain searchable, while data interconnections are not lost.

2021, Ortmeyer, Jochen, Schön, Florian, Herres-Pawlis, Sonja, Jung, Nicole, Bach, Felix, Liermann, Johannes, Neumann, Steffen, Popp, Christian, Razum, Matthias, Koepler, Oliver, Steinbeck, Christoph

Als Fachkonsortium für die Chemie hat sich NFDI4Chem innerhalb der Nationalen Forschungsdateninfrastruktur (NFDI) gebildet. In diesem Beitrag stellt sich das Konsortium kurz vor und legt seine zentralen Ziele und wichtigsten Verbesserungen für das Forschungsdatenmanagement (FDM) in der Chemie sowie die praktischen Heraus-forderungen dar. Die Vision von NFDI4Chem ist die umfassende Digitalisierung und Vernetzung aller Prozesse im Umgang mit Forschungsdaten in der chemischen Forschung. Beginnend mit der Erzeugung der Daten, über deren Verarbeitung und Analyse bis hin zur Publikation wird eine modulare, vernetzte Infrastruktur aus Software-Tools, elektronischen Laborjournalen und Datenrepositorien entwickelt und bereitgestellt, die Forschende im Laboralltag unterstützt. Die Digitalisierung wird begleitet durch die Entwicklung von Minimalinformationen für Datenpublikationen, bestehend unter anderem aus Standards für Daten- und Metadatenformate sowie Ontologien zur semantischen Beschreibung. Seine Aufgaben verfolgt das NFDI4Chem-Konsortium wissenschaftsgeleitet und mit dem klaren Ziel, eine intuitiv und effizient nutzbare Infrastruktur zu entwickeln. Das Gestalten eines kulturellen Wandels, gemeinsam mit der wissenschaftlichen Community, zur Etablierung und Akzeptanz eines FAIRen Umgangsmit Daten ist daher ein weiteres wichtiges Element der NFDI4Chem-Aktivitäten.

2023, Rossenova, Lozana, Schubotz, Moritz, Shigapov, Renat

The 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.

2023, Hagemann-Wilholt, Stephanie

In 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.