Filters

2020 - 202311

More filters

2021 - 202411
Reset filters

Settings

End date: 2024 × Start date: 2020 × Start date: 2020 × Subject: Research Data Management ×

Search Results

Now showing 1 - 10 of 11
  • Thumbnail Image
    Item
    A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts
    (Weinheim : Wiley-VCH, 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
  • Thumbnail Image
    Item
    Adamant: a JSON schema-based metadata editor for research data management workflows [version 1; peer review: 2 approved]
    (London : F1000 Research Ltd, 2022) Chaerony Siffa, Ihda; Schäfer, Jan; Becker, Markus M.
    The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant.
  • Thumbnail Image
    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, Laurence
    This 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.
  • Thumbnail Image
    Item
    Creation 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, Iryna
    The 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.
  • Thumbnail Image
    Item
    Development 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, Sören
    The 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.
  • Thumbnail Image
    Item
    The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI
    (Hannover : TIB Open Publishing, 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.
  • Thumbnail Image
    Item
    Isn'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, 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.
  • Thumbnail Image
    Item
    nestor endorsement of TRUST Principles
    (Meyrin : CERN, 2020-09-25) Arnold, Denis; Lindlar, Michelle; Recker, Jonas; Schoger, Astrid; Schumann, Natascha
    Nestor - 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
  • Thumbnail Image
    Item
    Train-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, Ulrike
    Im 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
  • Thumbnail Image
    Item
    Moving towards FAIRness in Research Data and Software Management
    (Meyrin : CERN, 2020-07-03) Kraft, Angelina
    Presentation during the Thüringer FDM Tage 2020 within the workshop "FAIR Research Software and Beyond: How to make the most of your code".