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- ItemDer Umgang mit Forschungsdaten an der Leibniz Universität Hannover : Auswertung einer Umfrage und ergänzender Interviews 2015/16(Hannover : Technische Informationsbibliothek, 2016) Hauck, Reingis; Kaps, Reiko; Krojanski, Hans Georg; Meyer, Anneke; Neumann, Janna; Soßna, VolkerIm Kontext des Projekts „Entwicklung eines institutionellen Konzepts zum Forschungsdatenmanagement an der Leibniz Universität Hannover“ wurden 2015/16 eine universitätsinterne Online-Umfrage und ergänzende Interviews durchgeführt. Deren Ergebnisse ermöglichen einen Einblick in den derzeitigen Umgang mit Forschungsdaten und eine Abschätzung des Bedarfs an Beratung, Schulung und technischer Infrastruktur seitens des wissenschaftlichen Personals. In diesem Bericht werden die Daten präsentiert und ausgewertet.
- ItemMoving towards FAIRness in Research Data and Software Management(Meyrin : CERN, 2020-07-03) Kraft, AngelinaPresentation during the Thüringer FDM Tage 2020 within the workshop "FAIR Research Software and Beyond: How to make the most of your code".
- ItemAdvancing Research Data Management in Universities of Science and Technology(Meyrin : CERN, 2020-02-13) Björnemalm, Matthias; Cappellutti, Federica; Dunning, Alastair; Gheorghe, Dana; Goraczek, Malgorzata Zofia; Hausen, Daniela; Hermann, Sibylle; Kraft, Angelina; Martinez Lavanchy, Paula; Prisecaru, Tudor; Sànchez, Barbara; Strötgen, RobertThe white paper ‘Advancing Research Data Management in Universities of Science and Technology’ shares insights on the state-of-the-art in research data management, and recommendations for advancement. A core part of the paper are the results of a survey, which was distributed to our member institutions in 2019 and addressed the following aspects of research data management (RDM): (i) the establishment of a RDM policy at the university; (ii) the provision of suitable RDM infrastructure and tools; and (iii) the establishment of RDM support services and trainings tailored to the requirements of science and technology disciplines. The paper reveals that while substantial progress has been made, there is still a long way to go when it comes to establishing “advanced-degree programmes at our major universities for the emerging field of data scientist”, as recommended in the seminal 2010 report ‘Riding the Wave’, and our white paper offers concrete recommendations and best practices for university leaders, researchers, operational staff, and policy makers. The topic of RDM has become a focal point in many scientific disciplines, in Europe and globally. The management and full utilisation of research data are now also at the top of the European agenda, as exemplified by Ursula von der Leyen addressat this year’s World Economic Forum.However, the implementation of RDM remains divergent across Europe. The white paper was written by a diverse team of RDM specialists, including data scientists and data stewards, with the work led by the RDM subgroup of our Task Force Open Science. The writing team included Angelina Kraft (Head of Lab Research Data Services at TIB, Leibniz University Hannover) who said: “The launch of RDM courses and teaching materials at universities of science and technology is a first important step to motivate people to manage their data. Furthermore, professors and PIs of all disciplines should actively support data management and motivate PhD students to publish their data in recognised digital repositories.” Another part of the writing team was Barbara Sanchez (Head of Centre for Research Data Management, TU Wien) and Malgorzata Goraczek (International Research Support / Data Management Support, TU Wien) who added:“A reliable research data infrastructure is a central component of any RDM service. In addition to the infrastructure, proper RDM is all about communication and cooperation. This includes bringing tools, infrastructures, staff and units together.” Alastair Dunning (Head of 4TU.ResearchData, Delft University of Technology), also one of the writers, added: “There is a popular misconception that better research data management only means faster and more efficient computers. In this white paper, we emphasise the role that training and a culture of good research data management must play.”
- ItemCreation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering(Laxenburg : IFAC, 2022) Sheveleva, Tatyana; Wawer, Max Leo; Oladazimi, Pooya; Koepler, Oliver; Nürnberger, Florian; Lachmayer, Roland; Auer, Sören; Mozgova, IrynaThe seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.
- Itemnestor endorsement of TRUST Principles(Meyrin : CERN, 2020-09-25) Arnold, Denis; Lindlar, Michelle; Recker, Jonas; Schoger, Astrid; Schumann, NataschaNestor - the German-speaking competence network for digital preservation - welcomes the TRUST principles as outlined in the white paper (https://doi.org/10.1038/s41597-020-0486-7) and joins the call for endorsement by the Research Data Alliance (https://www.rd-alliance.org/rda-community-effort-trust-principles-digital-repositories). nestor clearly sees the need for further development of the principles as they move into practise. As part of this, an ad-hoc WG TRUST discussed the principled and has released the statement "nestor endorsement of TRUST Principles". Benefits and recommendations at a glace • provides a common framework to facilitate discussion by all stakeholders • mnemonic helps to raise awareness • provides a low-threshold entry point • principles do not convey a sufficiently comprehensive picture of the requirements • preservation planning and suitable long-term preservation strategies are missing • TRUST Principles must be linked with established and accepted criteria suited to measuring trust-worthiness
- ItemDevelopment of a Domain-Specific Ontology to Support Research Data Management for the Tailored Forming Technology(Amsterdam [u.a.] : Elsevier, 2020) Sheveleva, Tatyana; Koepler, Oliver; Mozgova, Iryna; Lachmayer, Roland; Auer, SörenThe global trend towards the comprehensive digitisation of technologies in product manufacturing is leading to radical changes in engineering processes and requires a new extended understanding of data handling. The amounts of data to be considered are becoming larger and more complex. Data can originate from process simulations, machines used or subsequent analyses, which together with the resulting components serve as a complete and reproducible description of the process. Within the Collaborative Research Centre "Process Chain for Manufacturing of Hybrid High Performance Components by Tailored Forming", interdisciplinary work is being carried out on the development of process chains for the production of hybrid components. The management of the generated data and descriptive metadata, the support of the process steps and preliminary and subsequent data analysis are fundamental challenges. The objective is a continuous, standardised data management according to the FAIR Data Principles so that process-specific data and parameters can be transferred together with the components or samples to subsequent processes, individual process designs can take place and processes of machine learning can be accelerated. A central element is the collaborative development of a domain-specific ontology for a semantic description of data and processes of the entire process chain.
- ItemTrain-the-Trainer Konzept zum Thema Forschungsdatenmanagement - Version 3.1(Meyrin : CERN, 2020-12-18) Biernacka, Katarzyna; Buchholz, Petra; Danker, Sarah Ann; Dolzycka, Dominika; Engelhardt, Claudia; Helbig, Kerstin; Jacob, Juliane; Neumann, Janna; Odebrecht, Carolin; Wiljes, Cord; Wuttke, UlrikeIm Rahmen des BMBF-Projekts FDMentor wurde ein deutschsprachiges Train-the-Trainer Programm zum Thema Forschungsdatenmanagement (FDM) erstellt, das nach Projektende durch Mitglieder der UAG Schulungen/Fortbildungen der DINI/nestor-AG Forschungsdaten ergänzt und aktualisiert wurde. Die behandelten Themen umfassen sowohl die Aspekte des Forschungsdatenmanagements als auch didaktische Einheiten zu Lernkonzepten, Workshopgestaltung und eine Reihe von didaktischen Methoden. Die nun veröffentlichte dritte, überarbeitete und erweiterte Version des Train-the-Trainer-Konzepts enthält Einheiten zu Methoden und Materialien für Online-Veranstaltungen. Erste Erfahrungen aus bereits online durchgeführten Train-the-Trainer-Workshops sind zusätzlich in das Konzept eingeflossen. Die mit dieser Version eingeführten didaktischen Methoden für Online-Veranstaltungen sollen die geschulten Trainer*innen dabei unterstützen, ihre Schulungsangebote auch im virtuellen Raum lebendig und interaktiv zu gestalten und dient somit auch der weitergehenden Information der bereits geschulten Teilnehmer*innen. An English version of the "Train-the-Trainer Konzept zum Forschungsdatenmanagement" is available under https://doi.org/10.5281/zenodo.4071471
- Item“You say potato, I say potato” Mapping Digital Preservation and Research Data Management Concepts towards Collective Curation and Preservation Strategies(Bath : Digital Curation Centre, 2020) Lindlar, Michelle; Rudnik, Pia; Jones, Sarah; Horton, LaurenceThis paper explores models, concepts and terminology used in the Research Data Management and Digital Preservation communities. In doing so we identify several overlaps and mutual concerns where the advancements of one professional field can apply to and assist another. By focusing on what unites rather than divides us, and by adopting a more holistic approach we advance towards collective curation and preservation strategies.
- 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.