Leveraging measurement data quality by adoption of the FAIR guiding principles

Abstract

The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand. To ensure this data quality, the data management must be applied consequently throughout the complete Data Life-Cycle (DLC) and adhere to the FAIR guiding principles. In the two research consortia NFDI4Ing and the Cluster of Excellence “Internet of Production,” we investigate approaches to increase the measurement of data quality by integrating the FAIR guiding principles in all data management activities of the DLC. To facilitate the uptake of the FAIR guiding principles, we underline the significance of FAIR data for the reuse of high-quality data. Second, we are introducing a harmonised DLC to streamline data management activities. Third, we concisely review current trends and best practices in FAIR-aware data management and give suggestions for implementing the FAIR guiding principles.