ALBERT

Description: Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams. Digital technology use in agriculture and agrifood systems research accelerates the production of multidisciplinary data, which spans genetics, environment, agroecology, biology, and socio-economics. Quality labeling of data secures its online findability, reusability, interoperability, and reliable interpretation, through controlled vocabularies organized into meaningful and computer-readable knowledge domains called ontologies. There is currently no full set of recommended ontologies for agricultural research, so data scientists, data managers, and database developers struggle to find validated terminology. The Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture harnesses international expertise in knowledge representation and ontology development to produce missing ontologies, identifies best practices, and guides data labeling by teams managing multidisciplinary information platforms to release the FAIR data underpinning the evidence of research impact. The deployment of digital technology in Agriculture and Food Science accelerates the production of large quantities of multidisciplinary data. The Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture harnesses the international ontology expertise that can guide teams managing multidisciplinary agricultural information platforms to increase the data interoperability and reusability. The CoP develops and promotes ontologies to support quality data labeling across domains, e.g., Agronomy Ontology, Crop Ontology, Environment Ontology, Plant Ontology, and Socio-Economic Ontology.

Description: Essential Biodiversity Variables (EBV) are fundamental variables that can be used for assessing biodiversity change over time, for determining adherence to biodiversity policy, for monitoring progress towards sustainable development goals, and for tracking biodiversity responses to disturbances and management interventions. Data from observations or models that provide measured or estimated EBV values, which we refer to as EBV data products, can help to capture the above processes and trends and can serve as a coherent framework for documenting trends in biodiversity. Using primary biodiversity records and other raw data as sources to produce EBV data products depends on cooperation and interoperability among multiple stakeholders, including those collecting and mobilising data for EBVs and those producing, publishing and preserving EBV data products. Here, we encapsulate ten principles for the current best practice in EBV-focused biodiversity informatics as ‘The Bari Manifesto’, serving as implementation guidelines for data and research infrastructure providers to support the emerging EBV operational framework based on trans-national and cross-infrastructure scientific workflows. The principles provide guidance on how to contribute towards the production of EBV data products that are globally oriented, while remaining appropriate to the producer's own mission, vision and goals. These ten principles cover: data management planning; data structure; metadata; services; data quality; workflows; provenance; ontologies/vocabularies; data preservation; and accessibility. For each principle, desired outcomes and goals have been formulated. Some specific actions related to fulfilling the Bari Manifesto principles are highlighted in the context of each of four groups of organizations contributing to enabling data interoperability - data standards bodies, research data infrastructures, the pertinent research communities, and funders. The Bari Manifesto provides a roadmap enabling support for routine generation of EBV data products, and increases the likelihood of success for a global EBV framework.

Description: As part of the United Nations support to member countries in the development of the Sustainable Development Goals and following on from UNEA Resolution 1/4, UNEP organized an expert workshop on integrated indicators and the data revolution. The main aim was to develop integrated indicators which could support multiple goals and targets, using semantic networks and ontologies, relevant up‐ to‐date information and where needed big data derived from earth observation and mobile platforms. The multi‐disciplinary nature of large‐scale monitoring creates a complex collaborative setting characterised by a broad and varied knowledge‐base. Ensuring that entities in this environment are clearly represented on a semantic level can greatly enhance the gathering, retrieval, querying, handling, sharing, analysis, and reuse of data by diverse systems and communities, and ultimately the generation of indicators based on a common understanding and set of protocols. The discipline of ontology has much to contribute towards this aim in information‐rich systems. An ontology attempts to systematically identify, in simple (i.e. as ‘low‐level’ or empirical as possible) and precise terms, what the component entities in domains of interest are and how they relate to one another. This is done by creating a defined and logically‐structured vocabulary comprising classes and the relations between them. A series of six ontologies were used as a basis for the development of integrated indicators in six environmental areas, air quality, water quality, biodiversity, oceans, chemicals and waste, and land tenure. The aims of the workshop were to: i) determine the key semantics, ontologies and definitions for the six areas in order to develop common frameworks for integrated indicators across domains ii) Identify potential comparable baseline data and statistics for existing indicators and measurements, protocols for their use and where new and/or disaggregated data and statistics would be needed. The general conclusions from the meeting were: • Despite the numerous processes currently ongoing at the global, regional, sub‐regional and national levels which aim to promote and support the development and use of indicators, specific work on alignment of domains is needed to be able to develop indicators to measure progress in an integrated and systematic way. • The six focus areas, air quality, water quality, biodiversity, chemicals and waste, land tenure and oceans, were found to be causally linked to all 17 proposed SDGs, and to underpin their successful delivery. • The complexity of interactions between thematic areas could be captured through a core set of integrated indicators based on well‐aligned domain ontologies. • To fully support the SDGs, additional ontologies will need to be developed, for example in land and common resources.

Description: The meanings behind the terms used in the SDGs, their targets, and their indicators are often multifaceted, reflecting the diverse community of stakeholders involved in the SDG process. Consequently, there is a need to represent these various shades of meaning in a coherent way to prevent confusion when handling data and developing policy actions as well as enhancing the discoverability and management of SDG information and data across all the domains of knowledge. UNEP, in collaboration with experts in the field of ontology, is building a Sustainable Development Goals Interface Ontology (SDGIO) so that entities relevant to the SDGs can be logically represented, defined, interrelated, and linked to the corresponding terminology in glossaries and resources such as the UN System Data Catalogue and SDG Innovation platform. The SDGIO Working Group is now drawing input from domain specialists to shape the SDGIO to help the NSOs ensure that the SDG indicators are fully consistent across the SDGs.