Stop squandering data: make units of measurement machine-readable

Abstract

In 1999, when NASA's Mars Climate Orbiter missed its intended orbit and burned up in the Martian atmosphere, the media had a heyday over the reason: one team had used metric units in its thrust calculations, another, imperial. The navigation software that exchanged this information lacked a built-in process to check units. So when one team's software produced data in imperial units rather than the expected metric ones, the spacecraft was set on the wrong trajectory. The result was the loss of five years of effort and hundreds of millions of taxpayers' dollars.Two decades on, such problems persist. Researchers across fields often assume that their colleagues understand details without specifying them, and are therefore remiss when documenting units. Sometimes they leave them out entirely, provide ones that have multiple definitions or use units of convenience that have never been formally recognized.Humans struggle to interpret numbers with sloppy or missing units, and it is much more difficult when computers are involved. Most software packages, data-management tools and programming languages lack built-in support for associating units with numeric data (with the exception of the language F#). This means that information is essentially stored and managed as 'unitless' values. Disciplines including bioscience and aerospace engineering have adopted conventions for unit representation, such as the Unified Code for Units of Measure (UCUM) and the Quantities, Units, Dimensions, and Types (QUDT) Ontology. But there are no broadly agreed technical specifications for how to represent quantities and their associated units without confusing machines.There have been many calls in recent years to make data sets FAIR (Findable, Accessible, Interoperable and Reusable;

Managing Quantities and Units of Measurement in Code Bases

“…Recent initiatives such as the FAIR data principles [46] emphasis machineactionability of scientific data. With greater interoperability, industrial use of computational simulations and penetration of digitalisation through cyber-physical systems; there is an urgency to faithfully represent key properties of physical systems in code bases [1,47].…”

“…With ubiquitous digitalisation, and removal of humans in the loop, the need to faithfully represent and manipulate quantities in physical systems is ever increasing [1]. Popular programming languages allow developers to describe how to evaluate numeric expressions but not how to detect inappropriate actions on quantities.…”

“…Extensions to the Unified Modelling Language (UML) have been proposed to support quantities. SysML, for instance, is defined as an extension of a subset of UML to support systems engineering activities and has extensive support for quantities 1 . Unit checking and conversion can be undertaken before code is generated, either through a compilation workflow that leverages Object Constraint Language (OCL) expressions [11] or staged computation [12].…”

The Environmental Data Initiative: Connecting the past to the future through data reuse

“…Data are described by detailed metadata encoded in the Ecological Metadata Language (EML) standard (Jones, O'Brien, et al, 2019) and must pass a rigorous quality assessment before being published to the repository following community recommendations for best practices (Briney et al, 2020; Contaxis et al, 2022; Goodman et al, 2014; Hanisch et al, 2022; Roche et al, 2015; Whitlock, 2011). Although requirements to fulfill a basic EML document are minimal, EDI's user community agreed on requiring much broader and in‐depth metadata for any data to be archived and published as part of the main collection.…”

The Environmental Data Initiative: connecting the past to the future through data reuse

“…Data are described by detailed metadata encoded in the Ecological Metadata Language (EML) standard (Jones et al, 2019a) and must pass a rigorous quality assessment before being published to the repository following community recommendations for best practices (Whitlock, 2011, Goodman et al, 2014, Roche et al, 2015, Briney et al, 2020, Contaxis et al, 2022, Hanisch et al, 2022. Although requirements to fulfill a basic EML document are minimal, EDI's user community agreed on requiring much broader and in-depth metadata for any data to be archived and published as part of the main collection.…”