Ocean Data Management

Cloud Data: Challenges and Successes I Posters
Barry Eakins, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States and Kenneth S Casey, NOAA National Centers for Environmental Information, Silver Spring, MD, United States
Creating Data Synchronicity Across Ocean Microbiome Research I Posters
Elisha M Wood-Charlson, Lawrence Berkeley National Laboratory, Berkeley, CA, United States and Bonnie L Hurwitz, University of Arizona, Agricultural & Biosystems Engineering, Tucson, AZ, United States
Artificial Intelligence Systems for Advancing the Study of Aquatic Ecosystems I
Moritz S Schmid1, Eric Coughlin Orenstein2, Christian Briseño-Avena1 and Emlyn Davies3, (1)Oregon State University, Hatfield Marine Science Center, Newport, OR, United States(2)Scripps Institution of Oceanography, La Jolla, CA, United States(3)SINTEF Ocean, Trondheim, Norway
New Information Systems Tools for Implementing Autonomous Multisource, Multipoint Observing Systems I
Ian G Brosnan, NASA Ames Research Center, Earth Science Division, Moffett Field, CA, United States, Wu-Jung Lee, Applied Physics Laboratory University of Washington, Acoustics Department, Seattle, WA, United States, Laura Rogers, NASA Langley Research Center, Hampton, VA, United States and Robert Heitsenrether, NOAA Chesapeake, Chesapeake, VA, United States
Accelerating Ocean Science: Using Artificial Intelligence and Machine Learning to Gain New Insights I
Carrie Wall, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Jan Saynisch Wagner, GFZ - Potsdam, Potsdam, Germany and Christopher Irrgang, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geodesy and Remote Sensing, Potsdam, Germany
Accelerating Ocean Science: Using Artificial Intelligence and Machine Learning to Gain New Insights II Posters
Carrie Wall, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Jan Saynisch Wagner, GFZ - Potsdam, Potsdam, Germany and Christopher Irrgang, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geodesy and Remote Sensing, Potsdam, Germany
Accelerating Ocean Science: Using Artificial Intelligence and Machine Learning to Gain New Insights II Posters
Carrie Wall, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Jan Saynisch Wagner, GFZ - Potsdam, Potsdam, Germany and Christopher Irrgang, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geodesy and Remote Sensing, Potsdam, Germany
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More IV Posters
Alison R Gray, University of Washington, School of Oceanography, Seattle, WA, United States, Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, PA, United States and Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More I
Alison R Gray, University of Washington, School of Oceanography, Seattle, WA, United States, Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, PA, United States and Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States
Integrating, Disseminating, and Visualizing Quality Data at the Regional Scale to Support Resilient Coastal Communities I Panel
Gerhard Kuska, Mid Atlantic Regional Association Coastal Ocean Observing System, Newark, DE, United States, Debra Lee Hernandez, Southeast Coastal Ocean Observing Regional Association. SECOORA, Charleston, SC, United States, Barbara A Kirkpatrick, Gulf of Mexico Coastal Ocean Observing System, Sarasota, FL, United States and John Ruairidh Morrison, NERACOOS, Portsmouth, NH, United States
Data Science for Modern Oceanography: Statistics, Machine Learning, Visualization, and More II
Alison R Gray, University of Washington, School of Oceanography, Seattle, WA, United States, Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, PA, United States and Donata Giglio, University of Colorado Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, CO, United States
Training and Communication Across Disciplines and Methodological Approaches in Marine Science I Posters
Christian Lindemann1, Øyvind Fiksen1, Susanne Menden-Deuer2 and Aditee Mitra3, (1)University of Bergen, Department of Biosciences, Bergen, Norway(2)University of Rhode Island, Graduate School of Oceanography, Narragansett, RI, United States(3)Swansea University, Biosciences, Swansea, United Kingdom
Teaching with Data: Engaging Students in Learning Ocean Sciences Through Large Data Sets II Posters
Cheryl Lee Greengrove, University of Washington Tacoma Campus, Environmental Science, Tacoma, WA, United States, Denise Bristol, Hillsborough Community College, Biological and Earth Sciences, Ruskin, FL, United States, Anna Pfeiffer-Herbert, Stockton University, Pomona, United States, Logan D Brenner, Barnard College, New York, NY, United States and Janice D McDonnell, Rutgers University New Brunswick, Department of Youth Development, New Brunswick, NJ, United States
Advancing Technologies for the Future of Deep-Ocean Exploration II Posters
Katherine Lynn Croff Bell, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, United States, Brennan Phillips, University of Rhode Island, Narragansett, RI, United States, Kakani Katija, Monterey Bay Aquarium Research Institute, Moss Landing, CA, United States and Randi Rotjan, Boston University, Boston, MA, United States
Artificial Intelligence Systems for Advancing the Study of Aquatic Ecosystems II Posters
Moritz S Schmid1, Eric Coughlin Orenstein2, Christian Briseño-Avena1 and Emlyn Davies3, (1)Oregon State University, Hatfield Marine Science Center, Newport, OR, United States(2)Scripps Institution of Oceanography, La Jolla, CA, United States(3)SINTEF Ocean, Trondheim, Norway
Easy Access to Satellite and Other Oceanographic Data Sets Using the ERDDAP Data Server
Cara Wilson, NOAA/NMFS Southwest Fisheries Science Center, Monterey, CA, United States, Dale H Robinson, NOAA Southwest Fisheries Science Center, Environmental Research Division, Santa Cruz, CA, United States, Alice Marzocchi, University of Chicago, Geophysical Sciences, Chicago, IL, United States and Louis Clement, National Oceanography Centre, Southampton, United Kingdom
How Do We Make High-Resolution Ocean Simulations Useful to the Community?
Thomas W N Haine, Johns Hopkins University, Department of Earth & Planetary Sciences, Baltimore, MD, United States, Ryan Abernathey, Lamont-Doherty Earth Observatory, Palisades, NY, United States, Mattia Almansi, Johns Hopkins University, Baltimore, MD, United States, Alice Marzocchi, University of Chicago, Geophysical Sciences, Chicago, IL, United States and Louis Clement, National Oceanography Centre, Southampton, United Kingdom
Finding Just the Data You Need: New Developments in Online Oceanographic Data Discovery Tools
Phillippa Bricher, Southern Ocean Observing System, Institute of Marine and Antarctic Studies, Hobart, Australia, Patricia L Yager, University of Georgia, Athens, GA, United States, Patrick Gorringe, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden, Louis Clement, National Oceanography Centre, Southampton, United Kingdom and Alice Marzocchi, University of Chicago, Geophysical Sciences, Chicago, IL, United States
Pangeo: A Community Platform for Big-Data Geoscience
Richard P Signell, USGS Coastal and Marine Science Center Woods Hole, Woods Hole, MA, United States, Ryan Abernathey, Lamont-Doherty Earth Observatory, Palisades, NY, United States, Louis Clement, National Oceanography Centre, Southampton, United Kingdom and Alice Marzocchi, University of Chicago, Geophysical Sciences, Chicago, IL, United States
Establishing Active Fluorescence as a Primary Productivity Metric for the World's Coasts and Oceans
David J Suggett, University of Technology Sydney, Climate Change Cluster, Sydney, Australia, Philippe Daniel Tortell, University of British Columbia, EOAS, Vancouver, BC, Canada, Colleen B Mouw, University of Rhode Island, Narragansett, RI, United States and Camille Pagniello, Scripps Institution of Oceanography, University of California - San Diego, San Diego, CA, United States