Matching Data Life Cycle and Research Processes in Engineering Sciences

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.


Tobias Hamann M.Sc.  , Michèle Robrecht, Max Leo Wawer  , Jonas Maximilian Werheid  , Mario Moser, Marcos Alexandre Galdino  , Anas Abdelrazeq  , Roland Lachmayer, Robert Schmitt


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 data with increased complexity and diversity of subdisciplines. This article aims to provide clarity and structure in research processes integrating RDM, considering various models and viewpoints to present a research process for engineering sciences that is inherited from RDM processes. Therefore, interviews and workshops in different formats were used to gather insights about requirements in day-to-day work and research processes. As for further steps, the process will be evaluated on a later stage through a validation survey that will be taken into implementation for the Joint Assistant for Research in Versatile Engineering Sciences (Jarves).


Comment #116 Izadora Silva Pimenta @ 2024-06-20 02:47

As the responsible managing editor for this publication, I clarify that this preprint is still undergoing our regular conformity check revision. As soon as these processes are completed, this will be publicly informed through this channel.


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  • Published: 2024-06-20
  • Last Updated: 2024-06-20
  • License: Creative Commons Attribution 4.0
  • Subjects: Data Governance, Data Infrastructure, Data Literacy, Data Management Software
  • Keywords: Engineering Research, Research Data Management, Data Lifecycle, FAIR Data, Research data management processprocess
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