RDM system to ensure maturity - A use case from field experiments in engineering research

In engineering research, field experiments represent a large area of research across various disciplines, such as in the automotive [1]–[3] and robotics sectors [4], [5]. At the same time, they pose particular challenges for research data management (RDM). Collected data is often not reproducible or only reproducible to a very limited extent because, in contrast to experimental setups, the environmental conditions change dynamically during data collection [6], [7]. Comprehensive, structured and standard-compliant documentation of data collection is therefore essential for the quality, traceability andreusability of the generated research data. To support the implementation of sustainable RDM, systems can be used to document and visualize the data for further research-dependent processing and collaborative data exchange [8]. RDM systems can help to ensure the quality of the data throughsystematic documentation of the field conditions and the use of domain-specific standards. A secured RDM can make an effective contribution to quality assurance if it isbased on established standards of the discipline and integrates these into the technical infrastructure. By centralizing and standardizing access to field data, redundant datacollection efforts and the dependency on cost-intensive, dedicated experimental setups can be reduced. Within these RDM systems the data can be also shared during the project—not just at the end. Researchers can repurpose existing data to support new experiments or development workflows. Maturity models can be used for qualitative evaluation, which provide an assessment of the current status and further developments for improvement. The NFDI4Ing maturity models developed for engineering research enable an evaluation of the implementation of RDM in research projects [9]. Aligned to the research process, 5 individual, phase-oriented models offer a self-assessment by the researchers. The models show an evolutionary path from an intuitive implementation to a standardized and qualitativelyassured implementation of the RDM. In data collection and processing (analysis/synthesis), the focus of the RDM is onplanning the collection and documentation of this and the further processing [10]. For a defined, standards-oriented implementation of the RDM in these phases, community-specific guidelines and standards must be taken into account. This is aimed at the documentation of the experiments and the data organization of the collected data, see in Figure 1. The objective is to make the collected data comprehensible and reusable forproject exchange and the research community. This includes collaborative exchange within the project and making the data accessible. n order to ensure the requirements of an implementation of RDM defined according to the maturity model in the collection and further processing, RDM solutions can be used that take standards into account and whose implementation is applied in the system solutions. The Field data platform, developed as part of NFDI4Ing, is a suitable example of the practical implementation of such an approach [11]. This platform supports researchers in the standardized and consistent documentation and visualization of data from field experiments. It is based on domain-specific terminologies, enables context-related documentation of data acquisition and promotes reuse through definedinterfaces and structured metadata. Overall, the article shows how RDM systems effectively contribute to quality assurance in field-based research settings. It provides researchers a basis for community-oriented and standardized RDM.