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S4BioDiv 2021: 3rd International Workshop on Semantics for Biodiversity,
September 15, 2021, Bozen, Italy

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# The I-ADOPT Interoperability Framework for FAIRer data descriptions of
biodiversity

Barbara Magagna  Ilaria Rosati  Maria Stoica  Sirko Schindler  Gwenaelle
Moncoiffe  Anusuriya Devaraju  Johannes Peterseil  Robert Huber  Environment
Agency Austria, Vienna, Austria  Institute of Research on Terrestrial
Ecosystems, National Research Council, Montelibretti, Rome, Italy  University
of Colorado, Boulder, Colorado  Institute of Data Science, German Aerospace
Center, Jena, Germany  National Oceanography Centre, British Oceanographic
Data Centre, Liverpool, United Kingdom  Terrestrial Ecosystem Research Network
(TERN), University of Queensland, Australia  MARUM - Center for Marine
Environmental Sciences, University of Bremen, Bremen, Germany

(2021)

###### Abstract

Biodiversity, the variation within and between species and ecosystems, is
essential for human well-being and the equilibrium of the planet. It is
critical for the sustainable development of human society and is an important
global challenge. Biodiversity research has become increasingly data-intensive
and it deals with heterogeneous and distributed data made available by global
and regional initiatives, such as GBIF, ILTER, LifeWatch, BODC, PANGAEA, and
TERN, that apply different data management practices. In particular, a variety
of metadata and semantic resources have been produced by these initiatives to
describe biodiversity observations, introducing interoperability issues across
data management systems. To address these challenges, the InteroperAble
Descriptions of Observable Property Terminology WG (I-ADOPT WG) was formed by
a group of international terminology providers and data center managers in
2019 with the aim to build a common approach to describe what is observed,
measured, calculated, or derived. Based on an extensive analysis of existing
semantic representations of variables, the WG has recently published the
I-ADOPT framework ontology to facilitate interoperability between existing
semantic resources and support the provision of machine-readable variable
descriptions whose components are mapped to FAIR vocabulary terms. The I-ADOPT
framework ontology defines a set of high level semantic components that can be
used to describe a variety of patterns commonly found in scientific
observations. This contribution will focus on how the I-ADOPT framework can be
applied to represent variables commonly used in the biodiversity domain.

###### keywords:

observable properties variables biodiversity observations semantic
interoperability

##  1 Introduction

The interactions and diversity of organisms within and across the Earth’s
ecosystems play critical roles in the coevolution of the biosphere and the
broader Earth system. Human societies have evolved with and are part of this
complex system [1]. Societal development and well-being depend on ecosystem
services, or nature’s contributions to people [2]. Global biodiversity,
however, is declining faster than ever in human history [3]. Biodiversity
knowledge integration is urgently required to support responses towards its
conservation and a sustainable future [4].

Over the last decades, the need to share data and knowledge on biodiversity
has led to numerous local, regional, and global initiatives resulting in an
unprecedented biodiversity data mobilization [5]. Like many other domains,
biodiversity research has been transformed by a big data revolution. As the
creation of large volumes of complex data becomes routine, new approaches
using automated systems are required to find, access, combine, and interpret
their meaning [6]. The variety in data management practices and lack of common
standards led to an increase of heterogeneity at multiple levels and severely
impedes the integration of disparate data. Semantic approaches promise to
overcome this challenge by capturing rich representations of biodiversity data
to facilitate maximum interoperability and provide detailed descriptions for
re-use. This has led to a large collection of independent terminology
resources and tools across research infrastructures and communities as
explored below. Their complexity and diversity often overwhelm data managers
and users, ironically maintaining barriers to interoperability.

LifeWatch Italy is one of the national branches of LifeWatch ERIC, the
European e-science infrastructure for biodiversity and ecosystem research
(LifeWatch ERIC111https://www.lifewatch.eu/) [7]. Coordinated by the National
Research Council, LifeWatch Italy includes 35 partner institutions covering a
wide range of scientific disciplines (terrestrial, marine and freshwater
ecology, biology, zoology, botany, archaeobotany, geography, forestry,
agriculture). It seeks to reinforce integrated scientific research into
biodiversity by the development of a National Hub as the main access point to
data, apps, and eScience services for the management, aggregation, analysis,
and re-use of biodiversity and ecosystem research data. LifeWatch Italy has
already developed a Data Management System which can receive biodiversity
(e.g., checklists on geo-spatial distribution and abundance in Italy of
vegetal and animal species) and ecosystem data (e.g., abiotic variables),
including species traits data with grain size from individual to species
(e.g., morphological traits of phytoplankton). The system consists of two
major components, the LifeWatch profile for the dataset description, based on
the metadata standard EML 2.2.0222https://eml.ecoinformatics.org/ and a data
schema based on the Darwin Core standard333https://dwc.tdwg.org/ and
controlled vocabularies (e.g., LifeWatch Italy thesauri, EnvThes; in
EcoPortal444http://ecoportal.lifewatch.eu/), which provide a unified framework
for the information management of LifeWatch Italy. Thanks to a national
infrastructure project, new developments and upgrades are already planned
aiming specifically to improve the FAIRness of biodiversity and ecosystem
data.

The emerging European Long-Term Ecosystem, critical zone and socio-ecological
Research Infrastructure (eLTER RI555https://www.lter-europe.net/elter-esfri
[8]) is currently in the preparatory phase building on LTER Europe, the
regional network of ILTER. eLTER RI will adopt a fundamentally systemic
approach to observe and analyze the environmental system, encompassing
biological, geological, hydrological and socio-ecological perspectives. It
will allow in-situ, co-located acquisition and gathering of Essential
Variables ranging from bio-physicochemical to biodiversity and socio-
ecological data. Ecosystem change caused by long-term pressures and short-term
pulses will be investigated in a nested design across the scales covered by
the eLTER RI site network. Biodiversity data for a number of species groups
are collected by the contributing sites. Pilotto et al. [9] compiled 161
biodiversity time series mainly from European eLTER Sites focusing on
freshwater ecosystems taking the effort to harmonize and standardize the data.
A common infrastructure to harmonize species names still needs to be
implemented. Currently, e.g. the R-package
TaxonStand666https://cran.r-project.org/web/packages/Taxonstand/ is used to
harmonize species data based on ThePlantList777http://www.theplantlist.org/.
This will be further elaborated in the near future focusing on a harmonized
reporting of biodiversity data. For reporting of species information a common
data specification for eLTER [10] has been developed following the
recommendation of, e.g., the ICP Integrated Monitoring
programme.888https://unece.org/integrated-monitoring For the future,
biodiversity research and monitoring will be able to profit from a fully
standardized and harmonized set of biodiversity standard observations. With
this, many more sites could be used for various scientific analyses. The same
applies to a suitable set of environmental standard observations [11] that
help interpreting changes in biodiversity.

The British Oceanographic Data Centre (BODC) is managed and operated by the
UK’s National Oceanography Centre (NOC), an independent self-governing
organization with a status of charitable company limited by guarantee. BODC is
the marine component of the Natural Environment Research Council’s (NERC’s)
Environmental Data Centre network. Its mission is to develop, coordinate, and
provide specialist data services for marine science communities; to enable
innovative use and re-use of data; to ensure long-term curation of valuable
and unique marine data resources; and to champion Open Data. BODC runs the
NERC Vocabulary Server
(NVS)999https://www.bodc.ac.uk/resources/products/web_services/vocab/, an
internationally known and globally used infrastructure for the management of
and access to controlled vocabularies related to the marine science and
associated domains. Vocabularies served by the NVS underpin the SeaDataNet and
EMODnet infrastructure. They have been adopted by the Ocean Biodiversity
Information System (OBIS) to support data harmonization within and
interoperability of the OBIS Darwin Core MeasurementOrFact Extension [12].
These vocabularies, describing and identifying sensor and platform types and
models, variables measured or derived, units, methods, and many other
essential metadata elements, are used by an increasing number of scientific
marine data networks across the globe.

The information system PANGAEA101010https://www.pangaea.de/ is jointly managed
by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
(AWI) and the Centre for Marine Environmental Sciences (MARUM) at the
University of Bremen. PANGAEA is a certified (CoreTrustSeal), trustworthy
long-term operating repository providing continued access to more than 400,000
datasets from various sub-disciplines of Environmental Sciences. A large part
of PANGAEA’s dataset consists of biodiversity data, ranging from fossil
records to modern faunal and floral observations collected through research
infrastructures, research projects, and programs as well as individual
researchers. The required observed variables are managed in a controlled
vocabulary that is routinely undergoing an automated semantic annotation
process [13] to link, e.g., taxonomic vocabularies such as WORMS or ITIS to
harmonize species-related data. Access to biodiversity datasets which are
routinely tagged by a DOI is enabled through support for a number of
community-specific and cross-domain standards.

The Terrestrial Ecosystem Research Network
(TERN)111111https://www.tern.org.au/ collects and preserves biodiversity data
together with other critical terrestrial ecosystem data from the continental
scale to field sites across Australia to support researchers and policymakers’
immediate and long-term needs, including state and regional governments. TERN
has developed several valuable data services to support the discovery,
analysis, and re-use of data. One of the core services is enabling data
harmonization and integration through semantic data
model121212https://ternaustralia.github.io/ontology_tern/ (i.e., ontology) and
machine-interpretable vocabularies. The TERN data model is developed based on
the SOSA (Sensor, Observation, Sample, and
Actuator)131313https://www.w3.org/TR/vocab-ssn/ and comprises vocabularies for
representing ecological survey data, including biodiversity, vegetation, and
soil. The datasets are enriched with machine-interpretable vocabularies
representing variables measured, features, units, methods, instruments,
platforms, organization, and people. The vocabularies are available publicly
through the TERN Linked Data Services141414https://linkeddata.tern.org.au/.
They are developed either in-house based on community-endorsed specifications
(e.g., Australian Soil and Land Survey Field Handbook and AusPlots Rangelands
Survey Protocols Manual) or imported from external semantic resources (e.g.,
QUDT151515https://www.qudt.org/, GCMD161616https://earthdata.nasa.gov/earth-
observation-data/find-data/idn/gcmd-keywords, and NERC). The extensible data
model and rich collections of vocabularies are building blocks for improving
TERN data curation and discovery.

These are just a few regional implementations demonstrating a highly diverse
landscape of data management practices, even when dealing with the same type
of resources. This makes it clear that there are still some difficult hurdles
to overcome in order to achieve interoperability and convergence between
infrastructures in the same domain, and even more challenging across domains.
Initial progress has been made in providing machine-readable descriptions of
sensors and their observation types through the OGC’s Sensor Web Enablement
SensorML, Observations and Measurements (O&M), the Sensor Observation Service
(SOS) as well as the W3C’s/OGC’s Semantic Sensor Network (SSN) ontology
171717https://www.ogc.org/docs/is. This work has been further refined within
the OGC SensorThings API specification, as well as the upcoming version 3 of
O&M. However, “deep metadata” that further contextualizes observations (e.g.,
methodology or observable properties) is typically represented as coarsely
qualified classes. What exactly falls into these classes is currently
unconstrained and could be anything ranging from unstandardized free-text to
standardized descriptions accessible via fully resolvable URIs.

To address the challenge to properly model and describe observable properties
(also called variables), representative members of the above initiatives
decided to collaborate under the umbrella of the Research Data Alliance. The
Working Group InteroperAble Descriptions of Observable Property Terminology
(RDA I-ADOPT WG)181818https://www.rd-alliance.org/groups/interoperable-
descriptions-observable-property-terminology-wg-i-adopt was born in 2019 and
is now in its final phase. This group of international terminology providers
and data center managers set itself the objective to produce an
Interoperability Framework paving the way for seamless terminology alignment
for variable descriptions.

##  2 Methodology

This I-ADOPT WG has a strong focus on variables observed in environmental
research because it leverages existing efforts to accurately encode what was
measured, observed, derived, or computed in relation to Earth’s systems.
However, many of the principles it leans on are relevant to or connected with
other domains. The construction of the framework has been informed by a review
of current practices used in the community. The working group is also
iteratively testing and refining the framework through a set of in-depth use
cases. Much like a generic blueprint, the refined conceptual framework will be
a basis upon which terminology developers can formulate or refine their local
design patterns to more easily align with others and to avoid insurmountable
inconsistencies. By utilizing the recommendations of the I-ADOPT framework,
terminology developers may expand the applicability and interconnectivity of
their local resources in a collective attempt to uniformly represent complex
properties observed across the environmental sciences, from marine,
atmospheric, and terrestrial Earth sciences to biodiversity.

Figure 1: Tasks of the RDA WG I-ADOPT.

The I-ADOPT WG has been working for about two years to develop the common
framework for agreed-upon components to represent different aspects of
observable properties. The timeline followed over the last few years involved
completing six different tasks (see Figure 1):

  1. 1.

Collect user stories from the community about needs related to semantic
representation of observable properties involving nitrogen-related observable
properties, following a submission
template191919https://github.com/i-adopt/users_stories.

  2. 2.

Collect semantic representations from the community, including terminologies,
standard vocabularies, ontologies, and conceptual
models202020https://github.com/i-adopt/terminologies.

  3. 3.

Derive framework requirements by transforming user stories into concrete use
cases, categorizing use cases, and defining functional needs within each
category212121https://github.com/i-adopt/usecase_analysis.

  4. 4.

Analyze cataloged semantic artefacts to derive the framework’s information
requirements – a set of conceptual classes and relationships common to the
varying representation mechanisms, whether explicit or implicit.

  5. 5.

Develop the I-ADOPT Framework, including core variable components, their
relationships and definitions, and iterate on feedback from the community.

  6. 6.

(ongoing) Use the I-ADOPT Framework to develop aligned design patterns across
several participating terminology resources for selected use cases.

During the framework development, many potential framework components were
considered, discussed, and iterated upon, but only a small subset of those are
included in the current recommendation. Discussions of the different possible
components that were in the end not considered essential to the framework,
such as statistical measures, denominations of objects of interest, and
property dimensions and units, are documented through the group’s GitHub page
and supplemental documents. These efforts resulted in an ontology for
providing a machine-readable representation of the framework.

##  3 Results

The I-ADOPT Framework ontology222222https://w3id.org/iadopt/ont/ is a concise
ontology (see Figure 2) made of four classes (Variable, Property, Entity,
Constraint) and six object properties (hasProperty, hasObjectOfInterest,
hasContextObject, hasMatrix, hasConstraint, constrains). It is designed to
facilitate interoperability between existing variable description schemes
(including domain-specific ontologies, semantic models, and structured
controlled vocabularies) and to support further development of machine-
readable variable descriptions that re-use components mapped to FAIR
vocabulary terms. This first official version of the ontology has been
developed by a core group of terminology experts and users from the I-ADOPT
WG.

Figure 2: The I-ADOPT Framework.

The framework is variable-centric and can be used to describe result values of
any type of data acquisition events, be it a human-based observation, a
sensor-based measurement, a calculation, or a simulation. The Variable
describes WHAT has been observed, measured, simulated, or calculated
independently of WHERE (site description, geographical coordinates), HOW
(procedure, protocol) and WHEN (measurement time, time resolution) the data
acquisition has taken place, although in some cases the HOW might be important
to include in the variable description, as it might pose certain constraints
on it. This makes the application of this concept reusable in different
settings giving meaning to the determined value. Variables exist as concepts
in many vocabularies (see CF Standard
Names232323https://cfconventions.org/Data/cf-standard-names/47/build/cf-
standard-name-table.html or CSDMS Standard
Names242424https://csdms.colorado.edu/wiki/CSDMS_Standard_Names) to provide
metadata for the values provided in databases. They are also known as
observable properties (see Observable Property
Vocabulary252525http://registry.it.csiro.au/def/environment/_property) or
parameters (see BODC Parameter Usage Vocabulary
(P01)262626https://www.bodc.ac.uk/resources/vocabularies/parameter_codes/ and
EnvThes272727http://vocabs.lter-europe.net/EnvThes/). This variety of
approaches and the lack of a common representation strategy lead to a
situation where variable concepts from different terminologies are still not
directly comparable. Consequently, the data that is annotated with these
concepts is not interoperable and requires substantial manual efforts to
integrate.

In the following paragraphs, we explain the implementation of the framework by
use of an example282828 The following prefixes will be used throughout the
examples:  obo:<http://purl.obolibrary.org/obo/> ;
lifewatch:<http://thesauri.lifewatchitaly.eu/PhytoTraits/index.php?> ;
nerc:<http://vocab.nerc.ac.uk/collection/> ;
worms:<http://marinespecies.org/aphia.php?p=taxdetails&id=> , describing a
complex biodiversity variable that requires all of the components of the
framework. The variable description is “concentration of endosulfan sulfate in
wet flesh of ostrea edulis” (see Figure 3) and refers to the quantitative
result (i.e., requiring a magnitude and unit) of a measurement.

The I-ADOPT ontology, inspired by the atomisation approach of the Complex
Property Model [14] and The Scientific Variables
Ontology292929http://www.scientificvariablesontology.org/ [15, 16], conceives
the Variable as a compound concept consisting of at least one entity (the
ObjectOfInterest) and its Property, but very often includes further entities
contextualizing the target object of observation. Splitting the Variable into
constituent concepts enables the reuse and the mapping of these components in
the context of other variables. The ObjectOfInterest (endosulfan sulfate) can
be identified independently of an observable characteristic (concentration).
By concatenating the constituent elements with prepositions the preferred
label of a variable can be automatically created (compare with the right side
of the table in Figure 3). However, the I-ADOPT Framework does not provide any
recommendation how the label of the variable should be composed or which order
to follow as its main contribution targets machine-readability of the concept.
Not included in the variable description are units, measurement methods, and
time-related or geographical location information. Units are essential
information for describing measures, but a quantitative variable might be
expressed in different units which requires units be modeled independently of
variables. By isolating the Variable concept from these supplementary
information elements, it is possible to use the I-ADOPT framework in a broader
context interoperating with other representations of observations. In this way
the Variable with its explicit constituents can be conceived as an extended
and machine-readable conceptualization of the respective WHAT concept of these
models (e.g. ObservableProperty in SOSA303030https://www.w3.org/TR/vocab-ssn/,
Characteristic in OBOE313131https://github.com/NCEAS/oboe/).

Figure 3: Quantitative example: Concentration of endosulfan sufalte in Ostrea
edulis.

The Property (concentration) is a type of characteristic of the
ObjectOfInterest. This characteristic inheres in the object and exists as long
as the object exists. The cardinality of the object property hasProperty is
$1..1$, meaning that a Variable has to have exactly one Property. The Property
is in most cases a more generalized concept than the variable itself, as the
variable adds more detail to the property. This has the advantage that the
property term can be reused in other variable descriptions and further on
allows to identify similar measurements across different variables (e.g.,
concentration regardless of the substance observed).

The Entity is any object (endosulfan sulfate) or process (e.g., erosion) that
has a role in an observation. An Entity may play one of the following roles:
ObjectOfInterest, ContextObject, or Matrix. The roles of the entities in the
variable description are implemented as object properties in the I-ADOPT
ontology. Whether the involvement of a particular entity is meaningful enough
to include in the variable description, depends on the specific context.

  * •

hasObjectOfInterest: The ObjectOfInterest (endosulfan sulfate) is the Entity
whose property is observed. A Variable requires exactly one ObjectOfInterest
(cardinality: $1..1$).

  * •

hasContextObject: The ContextObject (Ostrea edulis) is the Entity that
provides additional background information regarding the ObjectOfInterest. A
variable can have more than one ContextObject (cardinality: $0..n$).

  * •

hasMatrix: The Matrix (flesh) is the Entity in which the ObjectOfInterest is
contained. A Variable might only have one Matrix (cardinality: $0..1$).

The Constraint (wet) limits the scope of the observation and confines the
context to a particular state. It describes properties of the involved
entities (flesh) that are relevant to the particular observation. Oftentimes
Constraints take the form of observations themselves, fixing other properties
that influence the main observation considered. It may constrain (constrains)
multiple entities playing the role of ObjectOfInterest, Matrix, or
ContextObject. Constraints are optional, so their the cardinality of the
object property hasConstraint is $0..n$.

The I-ADOPT ontology provides a framework to adequately address the data
interoperability requirements emphasized by the FAIR Guiding Principles [6].
Appropriate terms from FAIR semantic artefacts (like SKOS vocabularies or OWL
ontologies) should be used not only for the variable itself, but also for each
of the components of the variable description. This provides qualified
references to other metadata (FAIR Principle I3) where these exist or can be
easily created. In order to model a variable accurately, sound domain-specific
knowledge is required. This knowledge should be sufficiently reflected in the
form of a human-readable definition of the variable concept. Applying the
I-ADOPT framework at the metadata level means using the persistent and
resolvable identifier of the variable concept (e.g., Internationalized
Resource Identifier - IRI) instead of plain text information in the metadata
field addressing the observed property (FAIR Principle I2). This results in
semantically rich, machine-readable descriptions (metadata) of the dataset. At
the dataset level, the provision of FAIR data instead depends very much on the
distribution format of the datasets. In the case of spreadsheets, where the
quantitative values are described by the column headings, variable concept
IRIs should be used instead of labels (see Figure 3). In an ideal FAIR world,
data providers would distribute their datasets in a formal, accessible,
shared, and broadly applicable language for knowledge representation like RDF
(Resource Description Language) (FAIR Principle I1) as a way describing
resources on the web. Using RDF allows to use IRIs of the variable concept
directly in the triple statement describing the measurement result. Leadbetter
and Vodden [14] demonstrated that by FAIRifying metadata it is possible to
produce RDF files of the semantically enriched datasets. Because the variable
itself links to further descriptive elements, making implicit knowledge about
the measurement explicit, it is possible to apply faceted semantic data search
via SPARQL queries on data platforms, allowing the selection of datasets
involving a substance (like endosulfan sulfate) or a specific ObjectofInterest
(like Ostrea edulis).

Figure 4: Qualitative example: Shape of cell.

Many variables in the biodiversity domain are human-based observations with a
qualitative result value selected from a controlled list. Some elements for
the Occurrence, Organism, and Taxon metadata components of the Darwin Core
standard require qualitative values for which a few recommended SKOS
vocabularies were created by the TDWG Darwin Core Maintenance
Group323232https://dwc.tdwg.org/list/. LifeWatch Italy vocabularies provide
specific terminology for functional traits of several groups of aquatic
organism and alien species[17]. An example of how the I-ADOPT framework can be
applied to qualitative results of human-based observations is provided in
Figure 4 using a variable from the LifeWatch Phytotraits thesaurus. The
variable is called shape implicitly targeting cells, which is explained in the
definition of this specific variable. It is implemented as a SKOS concept and
has as more specific concepts qualitative values such as sphere, cube, or
ellipsoid. In dataset spreadsheets these values would normally populate the
cells of the variable column, but we recommend to use instead the IRIs of
these narrower concepts as manifestations of the observed value. However, a
more convenient way to store this type of information would be to use RDF for
the entire dataset. The constituent components of the variable shape (of cell)
are the more general concept of shape (the Property) and the ObjectOfInterest
the concept cell. It is not always possible to find more general properties
which could be reused in the context of other variables. The Darwin Core
metadata element degreeOfEstablishment would be referred to as a variable with
the IRI 333333https://dwc.tdwg.org/doe/ and decomposed into the Property
degreeOfEstablishment with the same IRI and the ObjectOfInterest
organism343434http://purl.obolibrary.org/obo/OBI_0100026.

##  4 Outlook

The RDA WG I-ADOPT will in its final phase produce guidelines for the
community on how to apply the framework. These will include design patterns to
generalize variable descriptions and their modelling for their reuse in
different settings (e.g., concentration of substance in biota). The authors of
this paper will evaluate the adoption of the I-ADOPT approach in their own
infrastructures. One first proof-of-concept will be the SKOS representation of
the eLTER Standard Observation variables based on the I-ADOPT Framework in
EnvThes. An important task will also be to work on mappings to existing
observation models (e.g., OBOE) and to foster the ongoing collaboration with
initiatives like OGC Observations and measurements Standard Working
Group353535https://github.com/opengeospatial/om-swg and DDI-
CDI363636https://ddi-
alliance.atlassian.net/wiki/spaces/DDI4/pages/860815393/DDI+Cross+Domain+Integration+DDI-
CDI+Review to evaluate how I-ADOPT might increase semantic interoperability of
the variable concept within those approaches.

###### Acknowledgements.

The authors like to thank the Research Data Alliance, eLTERplus, ENVRI-FAIR
(grant agreements No 871128, No 824068) and Alison Pamment (NCAS, UK) for
their support.

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