About FORUM-MD

The Forum on Metrology and Digitalization (FORUM-MD), established under the CIPM, brings together National Metrology Institutes (NMIs), Regional Metrology Organizations (RMOs) and international stakeholders to coordinate the digital transformation of metrology.

Its work focuses on developing a coherent digital infrastructure linked to the International System of Units (SI), including enabling machine-actionable data, supporting digital services, and addressing emerging challenges such as the use of AI in measurement systems.

Insights from those who attended FORUM-MD 2026

A central theme of the Forum was the alignment of key components of the SI digital ecosystem, including the SI Digital Framework (SIDF), the SI Reference Point (SIRP), and the Key Comparison Database (KCDB), to support the consistent exchange and interpretation of measurement data across digital systems.

Reflecting on the discussions, Peter Blattner, Chief Metrology Officer at METAS, said:

“Digital transformation not only boosts trust but also enhances efficiency, even for practical and administrative tasks.”

He emphasized that improved interoperability enables transparency and comparability, while supporting the structured, machine-readable use of data, including for the analysis of Calibration and Measurement Capabilities (CMCs).

The FORUM-MD supports these efforts by providing a platform for exchange and coordination on related activities and ensuring the consistent interpretation of measurement data across platforms.

For further information on this topic, watch Peter Blattner's keynote address to the Forum.

From FAIR data to digital calibration certificates

Another important area of focus in the Forum was the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) data principles to support consistent and reusable measurement data across laboratories and digital platforms.

In this context, the growing adoption of DCCs allows calibration data to be directly integrated into digital workflows.

Commenting on the practical implications of DCCs, Martin Koval, Czech Metrology Institute (CMI) said:

“Measurement results are no longer limited to values, units, and uncertainties, but include comprehensive contextual and semantic information. In the real field, this enables more efficient calibration-dependent processes, reduces human error, saves time, and supports advanced applications such as AI-driven analysis, digital twins and many others,”

To support the application of FAIR principles, the BIPM and the SIDF Task Group have been working on provenance versioning of the knowledge graph for the SIRP using the PROV ontology. Furthermore, it is looking into how to incorporate unit ‘one’ into the SIRP and how to improve website and Application Programming Interfaces (APIs) for the SIDF.

In response to needs identified during the Forum, the BIPM also plans to increase promotion and training on the SIRP and other digital services.

For more on this topic, watch Martin Koval and Rodolfo Souza of INMETRO, Brazil's keynote addresses for the Forum here.

Metrology and trustworthy AI

Finally, the Forum dedicated a day to an interactive workshop examining the role of AI in metrology, addressing both its use within measurement science and the application of metrological principles in the evaluation of AI systems.

The workshop highlighted applications of AI in the analysis of large calibration datasets and in supporting digital measurement records, with applications including medical diagnostics. A case study on the use of machine learning to support the trustworthiness of sensor networks illustrated how such approaches can help monitor performance in dynamic environments, including by identifying degradation in sensors deployed in challenging conditions.

Reflecting on these topics, Louise Wright, Chair of the FORUM-MD Task Group on Safe and Trustworthy AI at NPL, said:

“Metrology brings confidence to measurement data through careful definition of what is being measured, calibration and traceability to national standards, and the evaluation of uncertainty.” She noted that these same principles can be applied to AI systems, for example by ensuring that test data and training data are traceable, that testing addresses clearly defined risks, and that the outputs of AI models are accompanied by appropriate uncertainty evaluations to support confidence in their use.

For further detail on the use of AI within metrology, watch the keynote presentation by Martina Paul, Co-convenor of ISO/IEC JTC 1/SC 42 Joint Working Group 6 on Conformity Assessment of AI Systems.