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Abstract The Deep Ocean Observing Strategy (DOOS) is an international, community-driven initiative that facilitates collaboration across disciplines and fields, elevates a diverse cohort of early career researchers into future leaders, and connects scientific advancements to societal needs. DOOS represents a global network of deep-ocean observing, mapping, and modeling experts, focusing community efforts in the support of strong science, policy, and planning for sustainable oceans. Its initiatives work to propose deep-sea Essential Ocean Variables; assess technology development; develop shared best practices, standards, and cross-calibration procedures; and transfer knowledge to policy makers and deep-ocean stakeholders. Several of these efforts align with the vision of the UN Ocean Decade to generate the science we need to create the deep ocean we want. DOOS works toward (1) a healthy and resilient deep ocean by informing science-based conservation actions, including optimizing data delivery, creating habitat and ecological maps of critical areas, and developing regional demonstration projects; (2) a predicted deep ocean by strengthening collaborations within the modeling community, determining needs for interdisciplinary modeling and observing system assessment in the deep ocean; (3) an accessible deep ocean by enhancing open access to innovative low-cost sensors and open-source plans, making deep-ocean data Findable, Accessible, Interoperable, and Reusable, and focusing on capacity development in developing countries; and finally (4) an inspiring and engaging deep ocean by translating science to stakeholders/end users and informing policy and management decisions, including in international waters.
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This content will become publicly available on October 23, 2025
Towards an open-source model for data and metadata standards
Progress in machine learning and artificial intelligence promises to advance research and understanding across a wide range of fields and activities. In tandem, increased awareness of the importance of open data for reproducibility and scientific transparency is making inroads in fields that have not traditionally produced large publicly available datasets. Data sharing requirements from publishers and funders, as well as from other stakeholders, have also created pressure to make datasets with research and/or public interest value available through digital repositories. However, to make the best use of existing data, and facilitate the creation of useful future datasets, robust, interoperable and usable standards need to evolve and adapt over time. The open-source development model provides significant potential benefits to the process of standard creation and adaptation. In particular, data and meta-data standards can use long-standing technical and socio-technical processes that have been key to managing the development of software, and which allow incorporating broad community input into the formulation of these standards. On the other hand, open-source models carry unique risks that need to be considered. This report surveys existing open-source standards development, addressing these benefits and risks. It outlines recommendations for standards developers, funders and other stakeholders on the path to robust, interoperable and usable open-source data and metadata standards.
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- Award ID(s):
- 2334483
- PAR ID:
- 10550347
- Publisher / Repository:
- Open Science Framework
- Date Published:
- Format(s):
- Medium: X
- Institution:
- University of Washington
- Sponsoring Org:
- National Science Foundation
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