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4 pages, 1 figure.-- This article is licensed under a Creative Commons Attribution 4.0 International License, The ocean is mitigating global warming by absorbing large amounts of excess carbon dioxide from human activities. To quantify and monitor the ocean carbon sink, we need a state-of-the-art data resource that makes data submission and retrieval machine-compatible and efficient, This effort has been supported by EU Horizon 2020 through the EuroSea action (grant no. 862626), the Helmholtz Association through “Digital Earth” (grant no. ZT-0025), L.C.C. acknowledges the UERJ/Prociencia grant (2018-2021), M.H. acknowledge EU Horizon 2020 action SO-CHIC (grant N°821001). B.C., R.A.F., E.B., K.O’.B., and R.W. were supported by NOAA/OAR and NOAA/GOMO (fund ref. 100007298), B.T. was partly supported by Australia’s Integrated Marine Observing System (IMOS), S.K.L. acknowledges internal strategic funding from NORCE Climate, M.T. acknowledges support from the US NSF grant OCE-1840868 to SCOR. The PMEL and CICOES contributions are 5253 and 2020-1141, respectively

The use of uncrewed systems within NOAA as a research and operational tool is well-documented. The full benefits of uncrewed systems to achieving the NOAA mission have remained largely unrealized, primarily because of the programmatic nature of these deployments. Establishment of enterprise services to coalesce uncrewed systems activities is essential for NOAA to optimize the use and benefits of these systems. A robust and encompassing data enterprise is principal among these needed enterprise services, and is essential to the success of NOAA's uncrewed systems strategy. The NOAA Uncrewed Systems Strategic Implementation Plan presents a comprehensive multi-year plan of action that builds upon the framework established in the NOAA Uncrewed Systems Strategy. This manuscript describes the Uncrewed Systems Data Enterprise as outlined in the Implementation Plan. In Section II we describe the data enterprise milestones. The notional operational framework and planned case studies are described in Sections III and IV, respectively. The advantages of the data enterprise are summarized in Section V.

Data assembly in support of global data products, such as GLODAP, and submission of data to national data centers to support long-term preservation, demands significant effort. This is in addition to the effort required to perform quality control on the data prior to submission. Delays in data assembly can negatively affect the timely production of scientific indicators that are dependent upon these datasets, including products such as GLODAP. What if data submission, metadata assembly and quality control can all be rolled into a single application? To support more streamlined data management processes in the NOAA Ocean Acidification Program (OAP) we are developing such an application.This application has the potential for application towards a broader community.This application addresses the need that data contributing to analysis and synthesis products are high quality, well documented, and accessible from the applications scientists prefer to use. The Scientific Data Integration System (SDIS) application developed by the PMEL Science Data Integration Group, allows scientists to submit their data in a number of formats. Submitted data are checked for common errors. Metadata are extracted from the data that can then be complemented with a complete metadata record using the integrated metadata entry tool that collects rich metadata that meets the Carbon science community requirements. Still being developed, quality control for standard biogeochemical parameters will be integrated into the application. The quality control routines will be implemented in close collaboration with colleagues from the Bjerknes Climate Data Centre (BCDC) within the Bjerknes Centre for Climate Research (BCCR). This presentation will highlight the capabilities that are now available as well as the implementation of the archive automation workflow, and it’s potential use in support of GLODAP data assembly efforts.

Partnership between the private sector and the ocean-observing community brings exciting opportunities to address observing challenges through leveraging the unique strengths of each sector. Here, we discuss a case study of a successful relationship between the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) and Saildrone to instrument an unmanned surface vehicle (USV) in order to serve shared goals. This case study demonstrates that a private company working with a federal laboratory has provided innovative ocean-observing solutions deployed at regional scale in only a few years, and we project that this model will be sustainable over the long term. An alignment of long-term goals with practical deliverables during the development process and integrating group cultures were key to success. To date, this effort has expanded NOAA’s interdisciplinary observing capabilities, improved public access to ocean data, and paved the way for a growing range of USV applications in every ocean. By emphasizing shared needs, complementary strengths, and a clear vision for a sustainable future observing system, we believe that this case study can serve as a blueprint for public and private partners who wish to improve observational capacity. We recommend that the international scientific community continues to foster collaborations between the private sector and regional ocean-observing networks. This effort could include regional workshops that build community confidence through independent oversight of data quality. We also recommend that an international framework should be created to organize public and private partners in the atmospheric and oceanographic fields. This body would coordinate development of observational technologies that adhere to best practices and standards for sensor integration, verification, data quality control and delivery, and provide guidance for unmanned vehicle providers. Last, we also recommend building bridges between the private sector, the ocean-observing community, and the operational forecast community to consider the future of this new private sector, with goals to determine targeted ocean-observing needs; assess the appropriateness of USVs as science platforms, sensors, and data format standards; and establish usage and data quality control and distribution protocols for ocean observing and operational forecasting.

The oceans play a key role in global issues such as climate change, food security, and human health. Given their vast dimensions and internal complexity, efficient monitoring and predicting of the planet’s ocean must be a collaborative effort of both regional and global scale. A first and foremost requirement for such collaborative ocean observing is the need to follow well-defined and reproducible methods across activities: from strategies for structuring observing systems, sensor deployment and usage, and the generation of data and information products, to ethical and governance aspects when executing ocean observing. To meet the urgent, planet-wide challenges we face, methods across all aspects of ocean observing should be broadly adopted by the ocean community and, where appropriate, should evolve into “Ocean Best Practices.” While many groups have created best practices, they are scattered across the Web or buried in local repositories and many have yet to be digitized. To reduce this fragmentation, we introduce a new open access, permanent, digital repository of best practices documentation (oceanbestpractices.org) that is part of the Ocean Best Practices System (OBPS). The new OBPS provides an opportunity space for the centralized and coordinated improvement of ocean observing methods. The OBPS repository employs user-friendly software to significantly improve discovery and access to methods. The software includes advanced semantic technologies for search capabilities to enhance repository operations. In addition to the repository, the OBPS also includes a peer reviewed journal research topic, a forum for community discussion and a training activity for use of best practices. Together, these components serve to realize a core objective of the OBPS, which is to enable the ocean community to create superior methods for every activity in ocean observing from research to operations to applications that are agreed upon and broadly adopted across communities. Using selected ocean observing examples, we show how the OBPS supports this objective. This paper lays out a future vision of ocean best practices and how OBPS will contribute to improving ocean observing in the decade to come.

In the next decade the pressures on ocean systems and the communities that rely on them will increase along with impacts from the multiple stressors of climate change and human activities. Our ability to manage and sustain our oceans will depend on the data we collect and the information and knowledge derived from it. Much of the uptake of this knowledge will be outside the ocean domain, for example by policy makers, local Governments, custodians, and other organizations, so it is imperative that we democratize or open the access and use of ocean data. This paper looks at how technologies, scoped by standards, best practice and communities of practice, can be deployed to change the way that ocean data is accessed, utilized, augmented and transformed into information and knowledge. The current portal-download model which requires the user to know what data exists, where it is stored, in what format and with what processing, limits the uptake and use of ocean data. Using examples from a range of disciplines, a web services model of data and information flows is presented. A framework is described, including the systems, processes and human components, which delivers a radical rethink about the delivery of knowledge from ocean data. A series of statements describe parts of the future vision along with recommendations about how this may be achieved. The paper recommends the development of virtual test-beds for end-to-end development of new data workflows and knowledge pathways. This supports the continued development, rationalization and uptake of standards, creates a platform around which a community of practice can be developed, promotes cross discipline engagement from ocean science through to ocean policy, allows for the commercial sector, including the informatics sector, to partner in delivering outcomes and provides a focus to leverage long term sustained funding. The next 10 years will be “make or break” for many ocean systems. The decadal challenge is to develop the governance and cooperative mechanisms to harness emerging information technology to deliver on the goal of generating the information and knowledge required to sustain oceans into the future.

The U.S. Integrated Ocean Observing System (IOOS) Quality Assurance / Quality Control of Oceanographic Data (QARTOD) project has produced a series of real-time quality control manuals for specific core variables and other variables of interest to the oceanographic community. Not all core variables warrant a QARTOD manual. We consider each remaining variable for which a QARTOD manual has not been produced and discuss the potential for development of a manual in the near term.QARTOD QC tests are presently being implemented by IOOS Regional Associations. We review the challenges anticipated and encountered in this process and provide an example of a successful effort. Manuals are updated to ensure they remain accurate and relevant; feedback following implementation is a key component of the update process.QARTOD manuals are used internationally, and we will continue to expand our global collaborative efforts. These include a review of the Global Ocean Observing System essential ocean variables to consider further candidates for a QC manual, and partnerships supportive of QC, QA, measurement uncertainty, and best practices. QARTOD manuals are also used by universities to train the individuals who will implement the next generation of advanced QC tests.

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID.

14 pages, 4 tables, 4 figures.-- This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above., Funding for L-QJ and AK was from NOAA Ocean Acidification Program (OAP, Project ID: 21047) and NOAA National Centers for Environmental Information (NCEI) through NOAA grant NA19NES4320002 [Cooperative Institute for Satellite Earth System Studies (CISESS)] at the University of Maryland/ESSIC. BT was in part supported by the Australia’s Integrated Marine Observing System (IMOS), enabled through the National Collaborative Research Infrastructure Strategy (NCRIS). AD was supported in part by the United States National Science Foundation. AV and FP were supported by BOCATS2 Project (PID2019-104279GB-C21/AEI/10.13039/501100011033) funded by the Spanish Research Agency and contributing to WATER:iOS CSIC interdisciplinary thematic platform. MH was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement N°821001 (SO-CHIC)