Who is UnitsML for?

UnitsML is relevant to anyone who creates, exchanges, or processes scientific and engineering data that involves units of measure and quantities. This spans a wide range of disciplines and roles.

Target audiences

Standards developers and markup language designers

If you’re designing a markup language or data format for a scientific or engineering domain, UnitsML provides ready-made markup for units of measure. Instead of inventing your own unit representation, you can incorporate UnitsML elements directly into your schema.

Benefits:

  • Avoid reinventing unit representation for each domain
  • Gain interoperability with other markup languages that use UnitsML
  • Leverage existing unit definitions, conversions, and dimensional analysis

Examples: MatML (materials data), CML (chemical markup), AnIML (analytical chemistry), GML (geography), domain-specific engineering formats.

Data exchange architects

If you’re building systems that exchange scientific data between organizations or disciplines, UnitsML provides a common language for units that eliminates ambiguity.

Benefits:

  • Unambiguous unit identification across systems
  • Machine-readable conversion factors between unit systems
  • Dimensional validation to catch data errors before they propagate

Examples: Supply chain data exchange, laboratory information management systems, cross-disciplinary research data portals, emergency response data integration.

Database and software developers

If you’re building software that handles scientific measurements, UnitsML provides a structured data model for units that goes beyond simple string labels.

Benefits:

  • Precise unit definitions with symbols in 6 representations (ASCII, Unicode, HTML, LaTeX, MathML, id)
  • Built-in conversion algorithms via linear equation factors
  • Integration with UnitsDB for comprehensive unit data
  • Programmatic access via the unitsml-ruby gem

Examples: Scientific computing libraries, measurement instrument software, data validation tools, laboratory automation systems.

Scientific data managers

If you manage scientific data repositories or archives, UnitsML enables self-describing datasets where units are explicitly defined rather than assumed from context.

Modern laboratories generate vast quantities of measurement data from instruments using different unit conventions. When digitized analytical data is separated from metadata about how samples were collected and analyzed, the connection between scientific metadata and the data itself can be lost, rendering the data useless.

Benefits:

  • Datasets remain interpretable long after their original context is lost
  • Automated unit validation and conversion
  • Consistent metadata across heterogeneous data sources

Examples: Government science agencies, research data repositories, environmental monitoring databases.

Standards bodies and regulatory organizations

If your organization publishes standards involving scientific measurements, UnitsML provides an authoritative mechanism for encoding units in machine-readable form.

Benefits:

  • Normative reference for unit definitions
  • Alignment with SI and NIST standards
  • Machine-processable specifications

Examples: NIST, ISO committees, CalConnect, industry consortia, national metrology institutes.

Real-world use cases

Emergency response data integration

During emergencies, scientific data from disparate sources must be rapidly combined and interpreted. A radiation reading in “mSv/h” from one system must be correctly understood alongside a reading in “rem/h” from another. UnitsML provides the unambiguous definitions and conversion factors needed for reliable data fusion in time-critical situations.

Manufacturing supply chain specifications

Technical specifications that accompany products through the manufacturing supply chain include physical properties — dimensions, tolerances, material properties — expressed in various units. When a supplier specifies a tolerance as “±0.005 in” and the buyer’s system works in millimetres, UnitsML enables automatic, precise conversion without loss of accuracy.

Multi-disciplinary research collaboration

Research projects spanning physics, chemistry, engineering, and biology encounter a bewildering variety of units. A chemistry dataset might use angstroms and electronvolts while an engineering dataset uses nanometres and joules. UnitsML provides a common framework that makes all units explicitly defined and convertible.

Laboratory data management

Modern laboratories generate vast quantities of measurement data from instruments using different unit conventions. UnitsML enables:

  • Automatic unit validation when importing data
  • Consistent unit display across user interfaces
  • Reliable data aggregation from multiple instruments and vendors
  • Long-term data integrity through self-describing unit definitions

Geographic and environmental information systems

GIS and environmental monitoring systems work with spatial units (metres, degrees, radians), temporal units (seconds, days, years), and derived units (m/s, kg/m³, W/m²). UnitsML provides a unified schema for all these unit types.

Analytical chemistry and spectroscopy

The Analytical Information Markup Language (AnIML), developed by ASTM Subcommittee E13.15, is a real-world example of incorporating UnitsML into a domain-specific markup language for spectroscopy and chromatography data. AnIML uses UnitsML to handle all unit information, ensuring that analytical data remains self-describing and interchangeable across vendor platforms.

Shipbuilding and maritime classification

The Open Class 3D Exchange (OCX) standard encodes structural and geometric ship data in XML for model-based class approval. OCX uses UnitsML to represent physical quantities such as lengths, masses, and centres of gravity, so that shipyards and classification societies can exchange measurement data without ambiguity.

Industrial automation and product data dictionaries

UnitsML underpins unit representation in ECLASS, which provides semantic identifiers used by IEC/TS 62720 and the IEC Common Data Dictionary (CDD). This harmonization lets industrial automation systems reference units through stable, machine-readable IRDIs while remaining aligned with the SI.

How different users enter the UnitsML ecosystem

Standards developers
Incorporate UnitsML into your XML schema via reference, import, include, or redefine
Software developers
Use the unitsml-ruby gem for programmatic access, or parse UnitsML XML with standard libraries
Data managers
Add UnitsML markup to existing datasets; use UnitsDB for authoritative unit definitions
End users & researchers
Browse units interactively at UnitsDB; use software that supports UnitsML natively

Domains that benefit from UnitsML

Domain Common units Why UnitsML helps
Physics m, kg, s, A, K, mol, cd, J, N, Pa Base SI units and derived units with precise definitions
Chemistry mol/L, eV, Å, Da, g/mol Bridge between SI and domain-specific non-SI units
Engineering MPa, kW, mm, µm, rpm Mixed SI and non-SI units with conversion factors
Metrology All SI + derived Authoritative definitions aligned with BIPM
Environmental science µg/m³, ppb, Bq/m³ Complex derived units from multiple base quantities
Materials science GPa, J/m², nm, K/s Combined units with dimensional analysis
Aerospace ft, in, lbf, psi, Mach Mixed unit systems requiring precise conversion
Healthcare mg/dL, mmHg, mSv Medical units with non-trivial conversion factors
Analytical chemistry µg/L, mAU, m/z Instrument-specific units needing standardization

Adoption path

The use of UnitsML is completely optional — it’s designed to add value where it’s needed, not to force migration of existing systems. In many cases, existing systems have already developed domain-specific markup or codes for handling units, and it may not be practical to convert such systems to use UnitsML. However, the advantages — ready-made markup for units, improved interoperability — should provide a compelling case for new projects.

Many organizations adopt UnitsML incrementally:

  1. Reference — Start by referencing UnitsML definitions for units in new data formats
  2. Integrate — Add UnitsML elements to existing XML schemas
  3. Automate — Use UnitsDB and conversion tools for automated processing
  4. Standardize — Mandate UnitsML in data exchange agreements and specifications

Next steps