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A modular approach to developing interdisciplinary,
interoperable standards for geochemical data based on
the Semantic Sensor Network (SSN) Ontology.
Lesley Wyborn, Simon Cox, Kerstin Lehnert, Jens Klump
Summary
Interoperability across multiple scientific disciplines has generally had limited success, as most
standards are specific to a single community or discipline. However, many components of scientific
data are common to multiple disciplines, and if these can be identified and leveraged then valuable
foundations are laid for cross-discipline interoperability. A recent joint W3C and OGC standard - the
Semantic Sensor Network (SSN) ontology - specifies the semantics of sensors, observation, sampling,
and actuation, and supports a modular approach to the specification of the details of these elements
of a scientific information system. This separation of concerns then allows the relevant experts to
define community-endorsed standards for individual disciplines, with protocols and vocabularies in
each of the modular components, within a common overall framework. Where the same component
is used in many disciplines (e.g., geographical location, units of measure, colour, elements of the
periodic table, definition of magnetic properties) common (universal) reference standards,
protocols, can be established. This modular approach will ultimately facilitate interdisciplinary
science and lay the foundations for digital scientific data to be born connected to multiple
disciplines. An example of this approach is shown through the subdiscipline of geochemistry.
Abstract
Interoperability of data within a single scientific domain has been the focus of both technical data
managers and scientific discipline specialists for many decades. For example, geochemistry is widely
used in the field of Earth and environmental sciences, and several efforts have been undertaken to
standardise of geochemical data for publication within the geoscience discipline (e.g., Deines, 2003;
Staudigel et al., 2003; Potts, 2012; Goldstein et al., 2014). However, as they are designed for
standardising data in publications, they do not support use in data processing and analysis, and are
also limited in their capacity for interoperability with other disciplines. There has also been some
development of standards to allow data from multiple geochemical databases to be brought into a
coherent entity (e.g., EarthChem XML, IEDA 2018), but currently these are only local efforts and have
not yet been endorsed as agreed international standards.
Interoperability across multiple scientific disciplines has generally had limited success, mainly
because the standards within a discipline are too specific to the discipline that developed it.
However, many components of scientific data are common to multiple disciplines and if these can be
identified and leveraged, then valuable foundations are being laid for cross-discipline
interoperability. Observations and measurements are the basis for all empirical science. An
observation can be understood as an act designed to determine values of properties through
application of some procedure at a particular time and place, the result of an observation is strictly
an estimate of the true value, conditioned by procedure and circumstances. These concepts were
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crystallised by Cox in a conceptual model and encodings for observations and measurements
(“O&M”) (Cox, 2011, 2013; Cox and Taylor, 2015; ISO/TC 211, 2011) elaborating a pattern developed
originally for medical data by Fowler (1997). O&M defines a discipline-neutral vocabulary for an
observation and its properties and associated concepts. O&M was developed in parallel with models
and encodings for sensor data (Botts and Robin, 2013) and strongly influenced the design of the
initial SSN ontology (Compton et al., 2012).
In 2017 these evolved into a standardised version of the Semantic Sensor Network (SSN) ontology,
jointly issued by Open Geospatial Consortium (OGC) and World Wide Web Consortium (W3C) (Haller
et al., in press). The 2017 standard improves alignment of SSN with O&M, extends the scope of SSN
to cover sampling and also actuation, and also extracts the core elements into a simplified module
suitable for tagging web-pages. Of particular interest for scientific applications, the model for
sampling recognizes that most observations are made on extracts or subsets of the ultimate feature
of interest, and that description of the sampling process, and the nature of the relationships
between samples, and with the real-world feature are critical in characterizing scientific data. Where
the sample is taken from a real world object (i.e., is not synthetic), the details of the sample and the
feature that it is a sample of (e.g., its location) are kept separate. The properties that may be
observed and/or measured are provided by a domain model for features of interest and samples of
them (Figure 1).
Figure 1. The core structure of the SSN Ontology showing the common patterns used (taken from Figure 1 of Haller et al.
(in press)). For describing a geochemical analytical activity, modules common to most science are in red, those that have
their origins in chemistry are in blue and those sourced in the geosciences are in brown.
Discipline experts provide the detailed definitions of feature types, procedures, and the sensors or
samplers that implement them that are used in their community, within an overall common
framework which allows these to be related using a discipline-neutral language. And where the same
component is used in many disciplines (e.g., geographical location, units of measure, colour,
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elements of the periodic table, definition of magnetic properties) common ‘universal’ reference
standards, protocols, etc. can be set up, published through standard web technologies (e.g. as linked
data) and used across disciplines. This will ultimately facilitate interdisciplinary science.
However, many standards, vocabularies, protocols, etc. have already been developed in isolation
within a single scientific discipline. For example, methods of recording the precise geographic
location of the sample can be so variable between scientific disciplines that it is hard to collectively
access, aggregate and process all data that has been collected from the same sample locality.
Likewise, differing ways of providing unique identifiers to samples makes it hard to compare diverse
analytical results on the same sample from different disciplines.
For the more applied Earth and environmental sciences this is a common problem, particularly in the
subdisciplines of geochemistry and geophysics, which are fundamentally based on the application of
the sciences of chemistry and physics respectively to geological materials. But in these applied
subdisciplines, vocabularies, ontologies, protocols and procedures have often been developed
independent of the core parent science disciplines.
For geochemistry, the actual geological material that is analysed for chemical properties can be
described using the controlled and governed vocabularies developed by the International Union of
Geological Sciences (e.g., CGI IUGS, 2016) or alternatively the Observation Data Model 2 (ODM2;
Horsburgh, 2016). However, to describe measured values of the elements of the periodic table and
the procedures used, geoscientists can either develop their own (e.g., EarthChem XML,
Interdisciplinary Earth Data Alliance (IEDA), 2018), or else seek for equivalents developed by the
chemistry community (Figure 1). A Google search for ontologies and vocabularies for the periodic
table returned numerous results, and it is not possible for most geochemists to determine which are
authoritative or have been endorsed.
New initiatives within International Science Unions and CODATA (e.g., CODATA, 2016) are working
towards coordinating the International Science Unions to identify and endorse the more
authoritative standards (including vocabularies and ontologies) within each leading scientific
discipline. At the same time, initiatives within the OGC, W3C and the Research Data Alliance are
providing frameworks for the development of standards that enable translation of information
across disciplinary boundaries within the framework of the core SSN ontology as illustrated in Figure
1. Combined, these initiatives will start to enable interdisciplinary science and ensure that modern
digital data capture is born ‘connected’ to many disciplines.
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