Journal
SCIENTIFIC DATA
Volume 8, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41597-021-00889-9
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Funding
- National Science Foundation [OCE-1046297, 1559002, 1848576, 1948842, OCE-1437015, OCE-PLR-1425989]
- National Institutes of Health [T32AI141346]
- Cooperative Institute for Marine and Atmospheric Studies (CIMAS), a cooperative institute of the University of Miami
- NOAA [NA10OAR4320143]
- UK Natural Environment Research Council through its National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science [NE/R015953/1]
- NOAA Global Ocean Monitoring and Observation program [U8R1SE3-PRF]
- Directorate For Geosciences [1948842, 1559002] Funding Source: National Science Foundation
- Division Of Ocean Sciences [1559002, 1948842] Funding Source: National Science Foundation
- Division Of Ocean Sciences
- Directorate For Geosciences [1848576] Funding Source: National Science Foundation
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This study presents 971 globally distributed surface ocean metagenomes collected at high spatio-temporal resolution, generating a large amount of data and samples. These metagenomic libraries were collected as part of the Global Ocean Ship-based Hydrographic Investigations Program's biological initiative, aiming to directly quantify climate change impacts on ocean environments.
Detailed descriptions of microbial communities have lagged far behind physical and chemical measurements in the marine environment. Here, we present 971 globally distributed surface ocean metagenomes collected at high spatio-temporal resolution. Our low-cost metagenomic sequencing protocol produced 3.65 terabases of data, where the median number of base pairs per sample was 3.41 billion. The median distance between sampling stations was 26km. The metagenomic libraries described here were collected as a part of a biological initiative for the Global Ocean Ship-based Hydrographic Investigations Program, or Bio-GO-SHIP. One of the primary aims of GO-SHIP is to produce high spatial and vertical resolution measurements of key state variables to directly quantify climate change impacts on ocean environments. By similarly collecting marine metagenomes at high spatiotemporal resolution, we expect that this dataset will help answer questions about the link between microbial communities and biogeochemical fluxes in a changing ocean.
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