Data Management in Simulation Science:

Infrastructure, Tools, and Applications Tools, and Applications Tools, and Applications Tools, and Applications

• Tools, and Applications

Research Data Management (RDM) has gained significant traction

in recent years, being essential to allowing research data to be, e.g., findable, accessible, interoperable, and reproducible (FAIR), thereby fostering collaboration or accelerating scientific findings. We present solutions for RDM developed within the DFG-Funded Cluster of Excellence EXC2075 Data-Integrated Simulation Science (SimTech).

After an introduction to the scientific context and challenges faced by simulation scientists, we outline the general data management infrastructure and present tools that address these challenges. Exemplary domain applications demonstrate the use and benefits of the proposed data management software solutions. These are complemented by additional measures for enablement and dissemination to foster the adoption of these techniques.

1 Introduction

Research Data Management (RDM) has received increasing attention in recent years, e.g., to maintain research quality and reliability, promote collaboration among scientists, or accelerate impact of scientific findings. For instance, today, sharing and publishing research data is highly encouraged or even mandatory in conjunction with publishing scientific results or as part of research projects funded by agencies. The importance of research data management across a wide range of scientific disciplines is further reflected by large initiatives such as the National Research Data Infrastructure in Germany (NFDI)Footnote1, where domain-specific consortia devise

RDM solutions dedicated to the particular requirements of their field.