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: The construction of high capacity data sharing networks to support increasing government and commercial data exchange has highlighted a key roadblock: the content of existing Internet-connected information remains siloed due to a multiplicity of local languages and data dictionaries. This lack of a digital lingua franca is obvious in the domain of human food as materials travel from their wild or farm origin, through processing and distribution chains, to consumers. Well defined, hierarchical vocabulary, connected with logical relationships—in other words, an ontology—is urgently needed to help tackle data harmonization problems that span the domains of food security, safety, quality, production, distribution, and consumer health and convenience. FoodOn (http://foodon.org) is a consortium-driven project to build a comprehensive and easily accessible global farm-to-fork ontology about food, that accurately and consistently describes foods commonly known in cultures from around the world. FoodOn addresses food product terminology gaps and supports food traceability. Focusing on human and domesticated animal food description, FoodOn contains animal and plant food sources, food categories and products, and other facets like preservation processes, contact surfaces, and packaging. Much of FoodOn’s vocabulary comes from transforming LanguaL, a mature and popular food indexing thesaurus, into a World Wide Web Consortium (W3C) OWL Web Ontology Language-formatted vocabulary that provides system interoperability, quality control, and software-driven intelligence. FoodOn compliments other technologies facilitating food traceability, which is becoming critical in this age of increasing globalization of food networks.

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: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in The ISME Journal 12 (2018): 237–252, doi:10.1038/ismej.2017.165.

Description: Temperate coastal marine environments are replete with complex biotic and abiotic interactions that are amplified during spring and summer phytoplankton blooms. During these events, heterotrophic bacterioplankton respond to successional releases of dissolved organic matter as algal cells are lysed. Annual seasonal shifts in the community composition of free-living bacterioplankton follow broadly predictable patterns, but whether similar communities respond each year to bloom disturbance events remains unknown owing to a lack of data sets, employing high-frequency sampling over multiple years. We capture the fine-scale microdiversity of these events with weekly sampling using a high-resolution method to discriminate 16S ribosomal RNA gene amplicons that are 〉99% identical. Furthermore, we used 2 complete years of data to facilitate identification of recurrent sub-networks of co-varying microbes. We demonstrate that despite inter-annual variation in phytoplankton blooms and despite the dynamism of a coastal–oceanic transition zone, patterns of microdiversity are recurrent during both bloom and non-bloom conditions. Sub-networks of co-occurring microbes identified reveal that correlation structures between community members appear quite stable in a seasonally driven response to oligotrophic and eutrophic conditions.

Description: PLB is supported by the European Research Council Advanced Investigator grant ABYSS 294757 to Antje Boetius. AF-G is supported by the European Union’s Horizon 2020 research and innovation program (Blue Growth: Unlocking the potential of Seas and Oceans) under grant agreement no. (634486) (project acronym INMARE). This study was funded by the Max Planck Society. Further support by the Department of Energy Joint Genome Institute (CSP COGITO) and DFG (FOR2406) is acknowledged by HT (TE 813/2-1) and RA (Am 73/9-1).