Applications of Research Data Management at GESIS Data Archive for the Social Sciences

The chapter "Applications of Research Data Management at GESIS Data Archive for the Social Sciences" explores ways in which an archive - i.e. an organization whose work has a strong focus on preservation and dissemination of digital data - can become involved in research data management (RDM). The Data Archive looks back on a long history of working with researchers to make their data re-usable and accessible since 1960. Today it provides support for Research Data Management across the entire data lifecycle by offering a wide range of tools and services tailored to the needs of different types of stakeholders. The chapter gives an overview of selected tools and services offered in the areas of metadata and data documentation, data preparation, data publication, and long-term preservation. To illustrate how support for research data management plays out in different settings, three case studies for typical scenarios are presented: 1) The European Values Survey (EVS), a large international longitudinal survey studying basic human values across Europe. 2) The German Longitudinal Election Study (GLES), a national survey program with a comprehensive approach to gain insights into the German federal elections. 3) A data center in the health sector which decided to make data originally collected to support policy-making available to research

Sozialwissenschaftliche Forschungsdaten langfristig sichern und zugänglich machen: Herausforderungen und Lösungsansätze

Sozialwissenschaftlerinnen und -wissenschaftler sind gemäß der guten wissenschaftlichen Praxis dazu angehalten, den Forschungsprozess möglichst transparent zu halten, ihre Forschungsergebnisse reproduzierbar zu machen und im Sinne von „Open Science“ erhobene Daten für die Nachnutzung zur Verfügung zu stellen. Dies ist allerdings mit einigen Hürden verbunden. So müssen nachnutzbare Daten so dokumentiert werden, dass sie für Dritte auffindbar und verständlich sind. Gleichzeitig müssen personenbezogene Daten der Teilnehmenden ausreichend geschützt werden. Forschende sind hier auf Unterstützung angewiesen, um diesen Ansprüchen an die empirische Forschung gerecht zu werden. Dieser Artikel geht zunächst auf die Hürden bei der Bereitstellung von sozialwissenschaftlichen Forschungsdaten ein und stellt dann Dienstleistungen des GESIS Datenarchivs für Sozialwissenschaften vor, die Forschenden helfen, diese Hürden zu meistern. In accordance with the rules of good scientific practice, social scientists are encouraged to keep the research process as transparent as possible, to make their research results replicable, and – in the spirit of “Open Science” – to make collected data available for re-use. However, this is associated with a number of hurdles. For example, reusable data must be documented in such a way that it can be found and understood by third parties. At the same time, personal data of respondents must be adequately protected. Researchers need support to meet these demands on empirical research. The article first discusses the barriers to the provision of social science research data and afterwards presents special services of the GESIS Data Archive for Social Sciences, which help researchers to overcome these hurdles

nestor endorsement of TRUST Principles

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-worthines

Zertifizierung von Forschungsdatenrepositorien: Wege, Praxiserfahrungen und Perspektiven

Die DINI/nestor-AG Forschungsdaten führte am 5. März 2020 einen Workshop zum Thema Zertifizierung von Forschungsdatenrepositorien[1] an der Universitätsbibliothek Leipzig durch. Motiviert war die Veranstaltung durch den Wunsch, Teilnehmer*innen einen Überblick über relevante Zertifizierungsverfahren zu geben und Vorteile einer Zertifizierung herauszustellen. Gleichzeitig diente die Veranstaltung dem Austausch über Anforderungen und Unterstützungsbedarfe seitens der Repositorien. Trotz einer Reihe von Corona-bedingten Absagen und Vortragsausfällen verfolgten insgesamt 50 Teilnehmende die Vorträge und nahmen an der lebhaften Breakoutsession teil. Dieser Beitrag bereitet die Informationen und Anregungen aus den Diskussionen auf und skizziert erste Lösungsansätze zum Abbau identifizierter Hürden.   [1] https://www.forschungsdaten.org/index.php/Wiki-Seite_des_10._Workshops_der_DINI/nestor_A

Conceptualization of the use of Artificial Intelligence for Interdependencies Analysis in Requirements Engineering

The efficiency in product development is largely determined by the quality of the requirements and the ability of the product design and production planner to analyze them. Interdependencies between multiple requirements identified at an early stage enable a sustainable design of the product as well as the corresponding production system by increasing process efficiency as well as the effectiveness of development processes. However, the necessary analysis of complex interdependencies between requirements of a product and the corresponding production system is time-consuming, error-prone, and highly inefficient when performed manually. Current development processes are based on such manual processes for analyzing requirements in natural language and must therefore be adapted. This paper describes a methodical approach based on a semi-systematic literature review making the complexity of the interdependencies manageable by using existing approaches and methods in the field of model-based systems engineering (MBSE) as well as natural language processing (NLP). Thereby, a transition from informal requirements represented in natural language to analyzable and structured information, which enable interdependencies modeling for requirement chains, is described. A corresponding framework for analyzing interdependencies in the requirements engineering process is derived

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Fünf Jahre Bausteine Forschungsdatenmanagement : Rückblick und Neuerungen

In diesem Editorial anlässlich des fünfjährigen Bestehens der Bausteine Forschungsdatenmanagement blickt die Redaktion kurz zurück und erläutert Neuerungen bei der Ausgestaltung der Workflows und des Zeitschriftenprofils