Leveraging measurement data quality by adoption of the FAIR guiding principles

Robert H. Schmitt, M. Bodenbenner, Tobias Hamann, Mark P. Sanders, Mario Moser, Anas Abdelrazeq
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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.
采用 FAIR 指导原则,提高测量数据质量
对测量过程数据的分析和再利用是未来可持续和弹性制造的推动因素。保持测量数据的高质量对于最大限度地利用手头数据并使其发挥最大价值至关重要。为确保数据质量,数据管理必须贯穿整个数据生命周期(DLC),并遵循 FAIR 指导原则。在 NFDI4Ing 和 "生产互联网 "英才集群这两个研究联盟中,我们研究了通过将 FAIR 指导原则纳入 DLC 的所有数据管理活动来提高数据质量测量的方法。为了促进 FAIR 指导原则的实施,我们强调了 FAIR 数据对高质量数据再利用的重要意义。其次,我们正在引入一个统一的 DLC,以简化数据管理活动。第三,我们简要回顾了 FAIR 意识数据管理的当前趋势和最佳做法,并就实施 FAIR 指导原则提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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