New Science Research Resource: “Unifying Ocean Data into One Searchable Set” with SeaView
Ocean scientists will now find it easier to track deep-sea data from disparate sources: introducing SeaView, a new central home for ocean data that strings together five online databases.
The central idea of SeaView is to unify vast streams of data from different sets into one easily searchable set. The project, led by the Scripps Institution of Oceanography, is the ocean arm of a broader initiative called EarthCube. The latter effort is funded by the U.S. National Science Foundation to design and develop the cyberinfrastructure—information systems, databases, software, and tools—needed to support Earth and planetary sciences in the coming decades.
SeaView pulls data from five databases: Rolling Deck to Repository (R2R), Ocean Observatories Initiative, Biological and Chemical Oceanography Data Management Office (BCO-DMO), Ocean Biogeographic Information System (OBIS), and CLIVAR and Carbon Hydrographic Data Office (CCHDO). Together they encompass data on biological, chemical, physical, and geological properties of the majority of the world’s oceans.
To unify data from the different sources, researchers at SeaView first sought to understand why data have varied formats. Most of these sources are collections of data taken at sea by research cruises, moored sensors, and ocean gliders. Formatting differences in data strike at the nature of the research itself.
About Gary Price
Gary Price (email@example.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.