No abstract

The advent of large-scale cabled ocean observatories brought about the need to handle large amounts of ocean-based data, continuously recorded at a high sampling rate over many years and made accessible in near-real time to the ocean science community and the public. Ocean Networks Canada (ONC) commenced installing and operating two regional cabled observatories on Canada’s Pacific Coast, VENUS inshore and NEPTUNE offshore in the 2000s, and later expanded to include observatories in the Atlantic and Arctic in the 2010s. The first data streams from the cabled instrument nodes started flowing in February 2006. This paper describes Oceans 2.0 and Oceans 3.0, the comprehensive Data Management and Archival System that ONC developed to capture all data and associated metadata into an ever-expanding dynamic database. Oceans 2.0 was the name for this software system from 2006–2021; in 2022, ONC revised this name to Oceans 3.0, reflecting the system’s many new and planned capabilities aligning with Web 3.0 concepts. Oceans 3.0 comprises both tools to manage the data acquisition and archival of all instrumental assets managed by ONC as well as end-user tools to discover, process, visualize and download the data. Oceans 3.0 rests upon ten foundational pillars: (1) A robust and stable system architecture to serve as the backbone within a context of constant technological progress and evolving needs of the operators and end users; (2) a data acquisition and archival framework for infrastructure management and data recording, including instrument drivers and parsers to capture all data and observatory actions, alongside task management options and support for data versioning; (3) a metadata system tracking all the details necessary to archive Findable, Accessible, Interoperable and Reproducible (FAIR) data from all scientific and non-scientific sensors; (4) a data Quality Assurance and Quality Control lifecycle with a consistent workflow and automated testing to detect instrument, data and network issues; (5) a data product pipeline ensuring the data are served in a wide variety of standard formats; (6) data discovery and access tools, both generalized and use-specific, allowing users to find and access data of interest; (7) an Application Programming Interface that enables scripted data discovery and access; (8) capabilities for customized and interactive data handling such as annotating videos or ingesting individual campaign-based data sets; (9) a system for generating persistent data identifiers and data citations, which supports interoperability with external data repositories; (10) capabilities to automatically detect and react to emergent events such as earthquakes. With a growing database and advancing technological capabilities, Oceans 3.0 is evolving toward a future in which the old paradigm of downloading packaged data files transitions to the new paradigm of cloud-based environments for data discovery, processing, analysis, and exchange.

Ocean Networks Canada (ONC) operates ocean observatories on all three of Canada's coasts. The instruments produce 300 gigabytes of data per day with over 600 terabytes archived so far. The majority of this data is acoustic, both passive (335 TB) and active (20 TB). This demonstrates the unprecedented capability of cabled observatories to provide unlimited power and data for high bandwidth, continuous data acquisition. Handling this data is a challenge. Metadata, calibration, quality control, and access must be considered. The volume of data is too great for most users to handle. Even if they could store and process it, data transfer to users' computers is a limiting, and perhaps unnecessary step. To address these challenges, ONC has developed a data portal, known as Oceans 2.0, that includes on-demand user-configurable online previewing and processing and a computing “sandbox” where users can upload their own code to process the data. The data portal is now fully accessible by web services. The sandbox is a contained, secure environment with direct access to the data. This paper will present our experience and best practices, including use cases, from acquisition to adding value to the data with these new computing methods.

d u r a t io n in a c o u s t ic s e a b e d c l a s s if ic a t io n a n d CHARACTERZATION