A digitally enabled measurement infrastructure
- Traceability chains will be shortened, therefore reducing uncertainty
- Traceability to the SI will be embedded directly into measuring instruments
- In-situ calibration of sensors and local standards will be commonplace
The definitions of the SI base units are now designed to enable easier access for end users. New digital knowledge management and dissemination tools will bring primary standards closer to the end of the measurement chain and deliver lower measurement uncertainty for end user.
Digital and machine-readable calibration certificates held on global distributed ledgers will enable in-situ calibration of sensors and local standards, improving the provenance of measurements and simplifying traceability chains.
Understanding of complex systems
- Metrology will support a systems-based understanding of the world
- Metrology will support the growing prevalence of indirect, hybrid and proxy measurements
- Metrology will help combine data of different quality, provenance and time periods
Understanding complex systems is becoming more important as we try and manage and control climate change, biological processes and our ever growing, interconnected and interdependent infrastructure.
In order to develop and operate large, multi-scale and multi-level models of complex systems, we will need data quality frameworks to combine multimodal and multi-scale data.
By using a ‘metrology mindset’, complex systems can be analysed and uncertainties assigned, even though many of the measurements involved sit outside the SI.
Confidence in decision making
- Metrology will support decisions made by machine learning algorithms and explainable artificial intelligence
- Agile and responsive regulation will enable automated decisions to be made, even in safety critical situations
- Measurement scientists will have a responsibility to ensure that data is being used with integrity and impartiality
Confidence in decision-making will be reliant on the dynamic propagation of uncertainty through computationally intensive models or algorithms.
Taking advantage of increased measurement sensitivity, the seamless and secure sharing of data and increased computation power, needs to be combined with a sound metrological framework and understanding. This will lead to better decision making and the application of artificial intelligence and machine learning to new areas, therefore accelerating the process of discovery.