Measurement uncertainty is an increasingly important consideration in many applications demanding extreme performance levels. In the era of the internet of things and 5G connectivity we can learn more about device performance by utilising the increasing amount of data produced. These data require appropriate information infrastructure to facilitate continuous updating of device performance knowledge. This paper presents the results of a study which NPL undertook with a leading test and measurement device manufacturer to examine how measurement uncertainty propagates through the data traceability chain from national standards to end devices. A hierarchy of siloed calculations and heuristics did not enable a satisfactory metadata exchange within the dataflow to ensure an internally consistent calculation of measurement uncertainty. We therefore propose a novel measurement uncertainty network which contains a set of internally consistent measurement models, traceable to national standards and connected through common quantities. The network facilitates sharing and programmatic processing of measurement data with due regard to timeliness, privacy preservation and adherence to FAIR principles in measurement data exchange. An illustrative example of this network is presented with techniques to determine the best-fitting standard probability distribution for a given dataset and the resulting change in information content.

在许多要求极端性能水平的应用中，测量不确定度是一个越来越重要的考虑因素。在物联网和 5G 连接时代，我们可以通过利用产生的越来越多的数据来了解更多关于设备性能的信息。这些数据需要适当的信息基础设施来促进设备性能知识的持续更新。本文介绍了 NPL 与领先的测试和测量设备制造商进行的一项研究的结果，该研究旨在检查测量不确定性如何通过从国家标准到终端设备的数据可追溯性链传播。孤立计算和启发式的层次结构无法在数据流中实现令人满意的元数据交换，以确保测量不确定性的内部一致性计算。因此，我们提出了一种新颖的测量不确定度网络，该网络包含一组内部一致的测量模型，可追溯到国家标准并通过通用数量连接。该网络促进了测量数据的共享和程序化处理，同时适当考虑了测量数据交换的及时性、隐私保护和遵守 FAIR 原则。该网络的一个说明性示例使用技术来确定给定数据集的最佳拟合标准概率分布以及由此产生的信息内容变化。该网络促进了测量数据的共享和程序化处理，同时适当考虑了测量数据交换的及时性、隐私保护和遵守 FAIR 原则。该网络的一个说明性示例提供了用于确定给定数据集的最佳拟合标准概率分布以及由此导致的信息内容变化的技术。该网络促进了测量数据的共享和程序化处理，同时适当考虑了测量数据交换的及时性、隐私保护和遵守 FAIR 原则。该网络的一个说明性示例提供了用于确定给定数据集的最佳拟合标准概率分布以及由此导致的信息内容变化的技术。