Ocean FAIR Data Services

Frontiers in Marine Science

Peter Thijsse¹  Dick Schaap¹  Steve Diggs²  Alexander Smirnov³  Alessandra Giorgetti⁴  Helen Glaves⁵  Neville Smith⁶  Derrick Snowden⁷  Micah Wengren⁷  Danie Kinkade⁸  Tobias Spears⁹  Jose H. Muelbert^1,10  Shelley Stall^1,11  Antonio Novellino^1,12  Benjamin Pfeil^1,13  Stein Tronstad^1,14  Lesley Wyborn^1,15  Peter L. Pulsifer^1,16  Zhiming Zhao^1,17  Thomas Vandenberghe^1,18  Anton Van de Putte^1,18  Erin Robinson^1,19  Toste Tanhua^2,20  Valerie Harscoat^2,21  Thierry Carval^2,21  Sylvie Pouliquen^2,21  Jessica Hausman^2,22  Kevin O’Brien^2,23  Taco de Bruin^2,24  Marten Tacoma^2,24  Kenneth S. Casey^2,25  Eugene F. Burger^2,26  Justin J. H. Buck^2,27  Pip Bricher^2,28 

[1] 0MARIS Mariene Informatie Service, Voorburg, Netherlands;0Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United States;1Arctic Portal, Akureyri, Iceland;1Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Sgonico, Italy;2British Geological Survey, Nottingham, United Kingdom;2GODAE Ocean Services, Melbourne, VIC, Australia;3U.S. Integrated Ocean Observing System, Silver Spring, MD, United States;3Woods Hole Oceanographic Institution, Woods Hole, MA, United States;4Fisheries and Oceans, Science Branch, Maritimes Region Ocean Data and Information Section, Dartmouth, NS, Canada;4Instituto de Oceanografia, Universidade Federal do Rio Grande, Rio Grande, Brazil;5American Geophysical Union, Washington, DC, United States;5ETT, Genova, Italy;6Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway;6Norwegian Polar Institute, Tromsø, Norway;7National Computational Infrastructure, Australian National University, Canberra, ACT, Australia;7National Snow and Ice Data Center, University of Colorado Boulder, Boulder, CO, United States;8Informatics Institute, University of Amsterdam, Amsterdam, Netherlands;8Royal Belgian Institute for Natural Sciences, Brussels, Belgium;9Earth Science Information Partners, Boulder, CO, United States;GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany;IFREMER, Plouzané, France;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States;Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, WA, United States;NIOZ Royal Netherlands Institute for Sea Research, and Utrecht University, Texel, Netherlands;NOAA National Centers for Environmental Information, Silver Spring, MD, United States;NOAA Pacific Marine Environmental Laboratory, Seattle, WA, United States;National Oceanography Centre–British Oceanographic Data Centre, Liverpool, United Kingdom;Southern Ocean Observing System, University of Tasmania, Hobart, TAS, Australia;

关键词: FAIR;    ocean;    data management;    data services;    ocean observing;    standardization;   

DOI  :  10.3389/fmars.2019.00440

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown