Collaborative Research: CAIG: Using Deep Learning to Learn about the Deep Sea: Application — NSF Award to University of California
The deep sea is an epicenter of biogeochemical cycling that is globally important but poorly understood. Big data generated by emergent gene sequencing technology provides a new avenue to link genes with biological processes. In the deep sea, the vast majority of genes are unknown. This project will focus on methane se
| Award title | Collaborative Research: CAIG: Using Deep Learning to Learn about the Deep Sea: Application |
|---|---|
| Award ID | 2425835 |
| Awardee | University of California-Santa Barbara |
| City | SANTA BARBARA |
| State | CA |
| Amount obligated | $586,557 |
| Principal investigator | Andrew Thurber |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 09/15/2024 |
| Abstract | The deep sea is an epicenter of biogeochemical cycling that is globally important but poorly understood. Big data generated by emergent gene sequencing technology provides a new avenue to link genes with biological processes. In the deep sea, the vast majority of genes are unknown. This project will focus on methane seep systems. New microbial samples will be collected from methane seeps off the coast of Oregon and Washington. This research will employ a novel natural language processing artific |
| Source | NSF Awards |
$799/mo
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