Preprints
Filtering by Subject: Data Governance
Matching Data Life Cycle and Research Processes in Engineering Sciences
Tobias Hamann M.Sc., Michèle Robrecht, Max Leo Wawer, et al.
2024-06-20 Data Governance, Data Infrastructure, Data Literacy, Data Management Software
Research data management (RDM) has become increasingly significant, focusing on ensuring the most benefit from data creation. This especially applies within the engineering sciences, where many different sources generate big amounts of heterogeneous data. However, integrating RDM into day-to-day work is difficult. Thus, methods need to be defined to effectively and conveniently manage research [...]
How to teach good research data management to next generation researchers?
Syed Ashfaq Hussain Shah, Frank Petzold
2024-04-11 Data Governance, Data Infrastructure, Data Literacy
These days research work is subject to comply with FAIR principles. Additionally, it is subject to the practices of Open Science. Different stakeholders e.g. DFG are setting the goals of reproducible research work. This not only requires adequate handling of data but also the record of related information and practices during the research work. In this way, different tools and workflows are being [...]
A survey on the dissemination and usage of research data management and related tools in German engineering sciences
Tobias Hamann M.Sc., Amelie Metzmacher, Patrick Mund, et al.
2024-02-28 Data Governance, Data Infrastructure, Data Literacy, Data Management Software
As the amount of collected and analysed data increases, a need for data management arises to ensure its usability. This also applies in research. This challenge can be addressed by Research Data Management (RDM), which brings clear focus on the reusability of data. To understand the status quo of the application of research data management in engineering sciences in Germany, as well as possible [...]
PIA - A Concept for a Personal Information Assistant for Data Analysis and Machine Learning of Time-Continuous Data in Industrial Applications
Christopher Schnur, Tanja Dorst, Kapil Sajjan Deshmukh, et al.
2023-05-05 Data Governance, Data Literacy
A database with high-quality data must be given to fully use the potential of Artificial Intelligence (AI). Especially in small and medium-sized companies with little experience with AI, the underlying database quality is often insufficient. This results in an increased manual effort to process the data before using AI. In this contribution, the authors developed a concept to enable inexperienced [...]