Research Data Management - A Practical Introduction; V 1.0

The motivation for this best practice manual for research data management (RDM) resulted from many discussions and interviews that we conducted with users from the engineering and natural sciences communities. It became clear again and again that a practice-oriented guide for real research data management is missing and urgently needed. The available, more theoretically oriented presentations are generally only of limited help in practical application. For example, they do not explain how to specifically write a data management plan (DMP) and what third-party funding bodies look out for. Even for the practical operation of an electronic laboratory notebook (ELN), users are almost exclusively dependent on the manuals of the respective system, which represents a major hurdle for many users. Legal issues are also often a problem, for example when it comes to granting licenses or using external data. We have therefore decided to write this best practice handbook for RDM in order to close these gaps and provide free practical help for scientists. For example, we go into detail about how to write a DMP, explain the legal aspects of RDM that need to be taken into account in everyday life, show examples of how to use and operate the open-source electronic lab note-books JuliaBase, eLabFTW and Kadi4Mat, discuss aspects of data quality and analysis and which open-source software can be used for this purpose. We show how data can be exchanged and tracked between different ELNs and we demonstrate the publication of data. We also explain how research data can be used and reused for machine learning and what requirements must be met for this. Finally, we explain secure ways of permanently storing data and provide helpful tips and references for finding further information.