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\begin{document}

\title{Measurement Ontologies: A Field of Dreams for Essential Biodiversity
Variables}



\author[1]{Margaret O'Brien}%
\author[1]{Mark Schildhauer}%
\author[2]{Steven Chong}%
\author[2]{Robert Miller}%
\affil[1]{University of California, Santa Barbara}%
\affil[2]{University of California Santa Barbara}%


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  \date{May 28, 2024}


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\begin{abstract}
Essential Biodiversity Variables (EBVs) are state variables that lie
between primary measurements and high-level indicators, and are
necessary for assessment of the health and prognosis of Earth's
biosphere. EBVs represent the complete spectrum of biological diversity
from genes to ecosystems, and so are based on observations which
themselves are highly diverse, and typically human-collected or
analyzed. What is now sorely needed are structured dictionaries of
biological measurements that data collectors, curators and nascent
biodiversity programs can reference at all stages of planning and data
organization. Similarly, analysts working with data defined according to
these measurement dictionaries, require assurance that their results are
comparable across scales and institutions. Full understanding of primary
measurements will ideally require machine-readable, interpretable, and
interoperable descriptions of the measurement contents, collection
methods, data-typing, dimensions and associated units for physical
quantities, and specification of appropriate temporal and spatial
scales, plus the relationships among those attributes and facets of the
ecosystem. Formal ontologies, i.e. vocabularies built using modern
Semantic Web technologies, now provide the ideal tools and protocols for
structuring and operationalizing EBV primary measurements. Here we
illustrate an approach to apply these to existing data sets (both
primary and harmonized intermediates) using community-accepted
measurement ontologies under development. Such techniques can streamline
the discovery and integration of observations, assist with
calibration/validation checks required for automated or remote data
collection, and enable rigorous structured definitions for modeled or
remotely-sensed EBVs as these are developed.%
\end{abstract}%



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