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Omic BON is a thematic Biodiversity Observation Network under the Group on Earth Observations Biodiversity Observation Network (GEO BON), focused on coordinating the observation of biomolecules in organisms and the environment. Our founding partners include representatives from national, regional, and global observing systems; standards organizations; and data and sample management infrastructures. By coordinating observing strategies, methods, and data flows, Omic BON will facilitate the co-creation of a global omics meta-observatory to generate actionable knowledge. Here, we present key elements of Omic BON's founding charter and first activities.

 

This perspective outlines how authors of ocean methods, guides, and standards can harmonize their work across the scientific community. We reflect on how documentation practices can be linked to modern information technologies to improve discoverability, interlinkages, and thus the evolution of distributed methods into common best practices within the ocean community. To show how our perspectives can be turned into action, we link them to guidance on using the IOC-UNESCO Ocean Best Practice System to support increased collaboration and reproducibility during and beyond the UN Decade of Ocean Sciences for Sustainable Development. 

The UN Decade of Ocean Science for Sustainable Development (Ocean Decade) challenges marine science to better inform and stimulate social and economic development while conserving marine ecosystems. To achieve these objectives, we must make our diverse methodologies more comparable and interoperable, expanding global participation and foster capacity development in ocean science through a new and coherent approach to best practice development. We present perspectives on this issue gleaned from the ongoing development of the UNESCO Intergovernmental Oceanographic Commission (IOC) Ocean Best Practices System (OBPS). The OBPS is collaborating with individuals and programs around the world to transform the way ocean methodologies are managed, in strong alignment with the outcomes envisioned for the Ocean Decade. However, significant challenges remain, including: (1) the haphazard management of methodologies across their lifecycle, (2) the ambiguous endorsement of what is “best” and when and where one method may be applicable vs. another, and (3) the inconsistent access to methodological knowledge across disciplines and cultures. To help address these challenges, we recommend that sponsors and leaders in ocean science and education promote consistent documentation and convergence of methodologies to: create and improve context-dependent best practices; incorporate contextualized best practices into Ocean Decade Actions; clarify who endorses which method and why; create a global network of complementary ocean practices systems; and ensure broader consistency and flexibility in international capacity development. 

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.