Pragmatic Research Data Management for Heterogenous Sensor Data

Matthias Bodenbenner  , Tobias Hamann M.Sc.  , Mario Moser, Mark P. Sanders, Dominik Wolfschläger  , Robert H. Schmitt 

Research Data Management (RDM) is essential to facilitate open, effective, and accountable research. However, embedding RDM in researchers’ workflows is mainly hampered by two aspects: lack of incentives for RDM adoption, and limited knowledge about tools and services supporting RDM. We address this issue by implementing a standardized data description model and describe how we applied effective RDM practices to our research activity, Virtual Climatization. We demonstrate how implementing RDM actions can increase efficiency and effectiveness in the long run. This article showcases the success of our measures, including open-access publications, software artifacts, and reusability of data and services by other projects and researchers. We also highlight the importance of incentives, tools, and knowledge management for widespread RDM adoption. This paper provides insights into how effective RDM can enhance research continuity and depth, making it a landmark study in the engineering domain.