Wengren Micah

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Wengren
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Micah
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  • Article
    From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows.
    (Frontiers Media, 2019-05-21) Vance, Tiffany C. ; Wengren, Micah ; Burger, Eugene ; Hernandez, Debra ; Kearns, Timothy ; Medina-Lopez, Encarni ; Merati, Nazila ; O'Brien, Kevin ; O’Neil, Jon ; Potemra, James T. ; Signell, Richard P. ; Wilcox, Kyle
    Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.
  • Article
    Ocean FAIR data services
    (Frontiers Media, 2019-08-07) Tanhua, Toste ; Pouliquen, Sylvie ; Hausman, Jessica ; O’Brien, Kevin ; Bricher, Phillippa ; de Bruin, Taco ; Buck, Justin J. H. ; Burger, Eugene ; Carval, Thierry ; Casey, Kenneth S. ; Diggs, Stephen ; Giorgetti, Alessandra ; Glaves, Helen ; Harscoat, Valerie ; Kinkade, Danie ; Muelbert, Jose H. ; Novellino, Antonio ; Pfeil, Benjamin ; Pulsifer, Peter L. ; Van de Putte, Anton ; Robinson, Erin ; Schaap, Dick ; Smirnov, Alexander ; Smith, Neville ; Snowden, Derrick ; Spears, Tobias ; Stall, Shelley ; Tacoma, Marten ; Thijsse, Peter ; Tronstad, Stein ; Vandenberghe, Thomas ; Wengren, Micah ; Wyborn, Lesley ; Zhao, Zhiming
    Well-founded data management systems are of vital importance for ocean observing systems as they ensure that essential data are not only collected but also retained and made accessible for analysis and application by current and future users. Effective data management requires collaboration across activities including observations, metadata and data assembly, quality assurance and control (QA/QC), and data publication that enables local and interoperable discovery and access and secures archiving that guarantees long-term preservation. To achieve this, data should be findable, accessible, interoperable, and reusable (FAIR). Here, we outline how these principles apply to ocean data and illustrate them with a few examples. In recent decades, ocean data managers, in close collaboration with international organizations, have played an active role in the improvement of environmental data standardization, accessibility, and interoperability through different projects, enhancing access to observation data at all stages of the data life cycle and fostering the development of integrated services targeted to research, regulatory, and operational users. As ocean observing systems evolve and an increasing number of autonomous platforms and sensors are deployed, the volume and variety of data increase dramatically. For instance, there are more than 70 data catalogs that contain metadata records for the polar oceans, a situation that makes comprehensive data discovery beyond the capacity of most researchers. To better serve research, operational, and commercial users, more efficient turnaround of quality data in known formats and made available through Web services is necessary. In particular, automation of data workflows will be critical to reduce friction throughout the data value chain. Adhering to the FAIR principles with free, timely, and unrestricted access to ocean observation data is beneficial for the originators, has obvious benefits for users, and is an essential foundation for the development of new services made possible with big data technologies.