Description: Background The proportion of conserved DNA sequences with no clear function is steadily growing in bioinformatics databases. Studies of sequence and structural homology have indicated that many uncharacterized protein domain sequences are variants of functionally described domains. If these variants promote an organism's ecological fitness, they are likely to be conserved in the genome of its progeny and the population at large. The genetic composition of microbial communities in their native ecosystems is accessible through metagenomics. We hypothesize the co-variation of protein domain sequences across metagenomes from similar ecosystems will provide insights into their potential roles and aid further investigation. Methodology/Principal findings We calculated the correlation of Pfam protein domain sequences across the Global Ocean Sampling metagenome collection, employing conservative detection and correlation thresholds to limit results to well-supported hits and associations. We then examined intercorrelations between domains of unknown function (DUFs) and domains involved in known metabolic pathways using network visualization and cluster-detection tools. We used a cautious “guilty-by-association” approach, referencing knowledge-level resources to identify and discuss associations that offer insight into DUF function. We observed numerous DUFs associated to photobiologically active domains and prevalent in the Cyanobacteria. Other clusters included DUFs associated with DNA maintenance and repair, inorganic nutrient metabolism, and sodium-translocating transport domains. We also observed a number of clusters reflecting known metabolic associations and cases that predicted functional reclassification of DUFs. Conclusion/Significance Critically examining domain covariation across metagenomic datasets can grant new perspectives on the roles and associations of DUFs in an ecological setting. Targeted attempts at DUF characterization in the laboratory or in silico may draw from these insights and opportunities to discover new associations and corroborate existing ones will arise as more large-scale metagenomic datasets emerge.

Description: Large-scale monitoring endeavours generate a suite of informatics challenges ranging from data acquisition and handling to analysis and synthesis. In this complex space, there is a need to ensure meaningful communication between both human and machine agents which must be addressed early on in the design of a monitoring system in order to ensure accuracy and efficiency. Ontology offers a means to address this need and provide semantic clarity in service of a wide range of stakeholders (e.g. Pundt, 2002). This informal note aims to grant the reader some bearing on the nature and potential of ontology in information-rich monitoring environments. No attempt is made to present a complete overview of this field and this text should be considered nothing more than an invitation to delve deeper into the use of ontology in monitoring. Accessible reviews such as that by Madin et al. (2008) are excellent points to start further reading

Description: Development unavoidably impacts the ecosystems constituting Earth’s biosphere, often producing complex outcomes across a range of spatial and temporal scales. The interconnectivity of global ecosystems and their varied responses to disturbance necessitate great caution when using or encountering terms such as “sustainable” and “sustainability”. The Environment Ontology (ENVO; www.environmentontology.org) is coordinating with the Sustainable Development Goals Interface Ontology (SDGIO) to improve semantic representation of environments in the context of global development. In this talk, we will present progress towards this goal, emphasising the potential of ecosystem semantics in bridging and seeding new domain ontologies.

Description: Several resources and standards for indexing food descriptors currently exist, but their content and interrelations are not semantically and logically coherent. Simultaneously, the need to represent knowledge about food is central to many fields including biomedicine and sustainable development. FoodON is a new ontology built to interoperate with the OBO Library and to represent entities which bear a “food role”. It encompasses materials in natural ecosystems and food webs as well as humancentric categorization and handling of food. The latter will be the initial focus of the ontology, and we aim to develop semantics for food safety, food security, the agricultural and animal husbandry practices linked to food production, culinary, nutritional and chemical ingredients and processes. The scope of FoodON is ambitious and will require input from multiple domains. FoodON will import or map to material in existing ontologies and standards and will create content to cover gaps in the representation of food-related products and processes. As a robust food ontology can only be created by consensus and wide adoption, we are currently forming an international consortium to build partnerships, solicit domain expertise, and gather use cases to guide the ontology’s development. The products of this work are being applied to research and clinical datasets such as those associated with the Canadian Healthy Infant Longitudinal Development (CHILD) study which examines the causal factors of asthma and allergy development in children, and the Integrated Rapid Infectious Disease Analysis (IRIDA) platform for genomic epidemiology and foodborne outbreak investigation.

Description: In the current context of Biodiversity loss and climate change, it is more than ever necessary to adapt and develop our scientific practices to face these present-day global issues. Scientists, particularly biologists, have to define new protocols to optimize the tremendous amount of new data being generated and to analyse them. Monitoring Biodiversity is a complex problem because of its multiple facets and cross-domains links. The creation and use of ontologies to conceptualize those different aspects of Biodiversity is an efficient means for key stakeholders and policies makers to promote consistency and reliability of systems. For this purpose, the Environment Ontology (ENVO; http://www.environmentontology.org) is a community ontology for the concise and controlled description of environments. It is interoperating with other domain ontologies closely linked to the representation of biodiversity in order to better interface with efforts such as Darwin Core and initiatives to promote the achievement of the United Nations’ Sustainable Development Goals (SDGs). As part of the ENVO consortium, BiGAEOn is an ontology for biogeographic areas specifically. Biogeographic areas are the basic units used in Comparative Biogeography to produce classifications of biogeographic areas, here, bioregionalisation. BiGAEOn model describe and harmonize biogeographic entities (e.g. areas of endemism, endemic areas…) as well as their relationships. Hence, it provides a rigorous and simple framework that improves biogeographic analyses and interoperability between systems. In particular, BiGAEOn integrates formal descriptions of WWF ecoregions (http://www.worldwildlife.org). In this presentation, we will illustrate how our ontology fits current debates with a case study on Australia, since it’s the actual scene of the bioregionalisation revival.

Description: The publication and dissemination of best practices in ocean observing is pivotal for multiple aspects of modern marine science, including cross-disciplinary interoperability, improved reproducibility of observations and analyses, and training of new practitioners. Often, best practices are not published in a scientific journal and may not even be formally documented, residing solely within the minds of individuals who pass the information along through direct instruction. Naturally, documenting best practices is essential to accelerate high-quality marine science; however, documentation in a drawer has little impact. To enhance the application and development of best practices, we must leverage contemporary document handling technologies to make best practices discoverable, accessible, and interlinked, echoing the logic of the FAIR data principles [1].

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: Four years after the Genomic Observatories Network was formally established as a collaboration between the Group on Earth Observations Biodiversity Observation Network and the Genomic Standards Consortium, we review the development of the network. Considering institutional infrastructure, we note the growing role of omic observation in active and increasingly interlinked marine networks, with examples such as EMBRC/ASSEMBLE, International Long Term Ecological Research Network, AtlantOS, National Association of Marine Labs, Smithsonian MarineGEO, and Partnership on Observation of the Global Oceans. We also note some key human elements essential to meeting the networks' goals, address how the community is evolving, and why performing seemingly simple tasks within a broadly distributed community presents significant challenges even among those who have agreed to use standards. From the perspectives above, we review lessons learned from use cases that leverage Genomic Observatories Network, such as the Autonomous Reef Monitoring Structures (ARMS), Ocean Sampling Day (OSD) and myOSD, which included experiences with citizen science. Looking forward, we survey 1) promising new technologies for in situ biological observation (e.g., cheap 3D printed omics samplers), 2) progress towards adoption of omics methods in marine policy and conservation programs, and 3) opportunities that a Genomic Observatory brings, alone or embedded in a network, to address novel scientific questions and support Essential Biodiversity Variables, Essential Ocean Variables, and indices such as the Ocean Health Index. Given the data intensive nature of omics investigation, we note emerging cyberinfrastructure solutions, such as the Genomic Observatories Metadatabase (GeOMe), an open-access repository for geographic and ecological metadata associated with biosamples, and predictive modeling efforts, such as those of the Island Digital Ecosystem Avatar (IDEA) Consortium. Finally, we explore the potential of Genomic Observatories as components of high-resolution calibration sites. Such observatories would provide super-contextualized "data trusts" for machine learning and artificial intelligence applications that draw on multi-omic observation.

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.