Accessing BCO-DMO data
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BCO-DMO ERDDAP
Accessing BCO-DMO data |
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| griddap | Subset | tabledap | Make A Graph | wms | files | Accessible | Title | Summary | FGDC | ISO 19115 | Info | Background Info | RSS | Institution | Dataset ID | |
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| log in | [Metagenomic samples] - Metagenomic sample information, genetic accession identifiers (NCBI SRA, JGI IMG), and estimated gene copies from Orcas Island coastal waters (2 m depth) in May and June of 2021 (Collaborative Research: Rhythm and Blooms: Deciphering metabolic, functional and taxonomic interactions over the life cycle of a phytoplankton bloom) | This dataset contains NCBI Sequence Read Archive (SRA) accession numbers, DOE JGI Integrated Microbial Genomes & Microbiome (IMG/M) IDs, and estimated gene copies for metagenomic samples collected at Orcas Island, WA, USA Coastal Ocean (2m depth) from 5/27/21 to 6/18/21 collected as part of the following study.\n\nStudy abstract:\n\nFloating, single-celled algae, or phytoplankton, form the base of marine food webs. When phytoplankton have sufficient nutrients to grow quickly and generate dense populations, known as blooms, they influence productivity of the entire food web, including rich coastal fisheries. The present research explores how the environment (nutrients) as well as physical and chemical interactions between individual cells in a phytoplankton community and their associated bacteria act to control the timing of bloom events in a dynamic coastal ecosystem. The work reveals key biomolecules within the base of the food web that can inform food web functioning (including fisheries) and be used in global computational models that forecast the impacts of phytoplankton activities on global carbon cycling. A unique set of samples and data collected in 2021 and 2022 that captured phytoplankton and bacterial communities before, during, and after phytoplankton blooms, is analyzed using genomic methods and the results are used to interrogate these communities for biomolecules associated with blooms stages. The team mentors undergraduates, graduate students, and postdoctoral researchers in the fields of biochemical oceanography, genome sciences, and time-series multivariate statistics. University of Washington organized hackathons to develop publicly accessible portals for the simplified interrogation and visualization of 'omics data, accessible to high schoolers and undergraduates. These portals are implemented in investigator-led undergraduate teaching modules in the University of Rhode Island Ocean Classroom. The research team also returns to Orcas Island, WA, where the field sampling takes place, to host a series of annual Science Weekends to foster scientific engagement with the local community.\nPhytoplankton blooms, from initiation to decline, play vital roles in biogeochemical cycling by fueling primary production, influencing nutrient availability, impacting carbon sequestration in aquatic ecosystems, and supporting secondary production. In addition to influences from environmental conditions, the physical and chemical interactions among planktonic microbes can significantly modulate blooms, influencing the growth, maintenance, and senescence of phytoplankton. Recent work in steady-state open ocean ecosystems has shown that important chemicals are transferred amongst plankton on time-dependent metabolic schedules that are related to diel cycles. It is unknown how these metabolic schedules operate in dynamic coastal environments that experience perturbations, such as phytoplankton blooms. Here, the investigators are examining metabolic scheduling using long-term, diel sample sets to reveal how chemical and biological signals associated with the initiation, maintenance, and cessation of phytoplankton blooms are modulated on both short (hrs) and long (days-weeks) time scales. Findings are advancing the ability to predict and manage phytoplankton dynamics, providing crucial insights into ecological stability and future oceanographic sampling strategies. Additionally, outcomes of this study are providing a new foundational understanding of the succession of microbial communities and their chemical interactions across a range of timescales. In the long term, this research has the potential to identify predictors of the timing of phytoplankton blooms, optimize fisheries management, and guide future research on carbon sequestration.\n\ncdm_data_type = Other\nVARIABLES:\nDateID_PT (unitless)\n... (33 more variables)\n | BCO-DMO | bcodmo_dataset_984169_v1 | ||||||||||||
| log in | [Subsurface Nitrospirota and Nitrospinota Origins] - Collection of subsurface bacteria Nitrospirota and Nitrospinota genome data including IMG and NCBI accessions for sequence datasets in June 2021 (Slow Life in Crust project) (Microbial activity in the crustal deep biosphere) | The phyla Nitrospirota and Nitrospinota have received significant research attention due to their unique mitrogen metabolisms important to biogeochemical and industrial processes. These phyla are common inhabitants of marine and terrestrial subsurface environments and contain members capable of diverse physiologies in addition to nitrite oxidation and complete ammonia oxidation. We used phylogenomics and gene-based analysis with ancestral state reconstruction and gene-tree-species tree reconciliation methods to investigate the life histories of these two phyla. This dataset includes list of previously-published sequence datasets that were used for the analysis. The data and interpretations are published at DOI 10.1038/s41396-023-01397-x. Additional metadata such as NCBI accessions, assembly release dates, and NCBI taxon ids were added in December 2024.\n\ncdm_data_type = Other\nVARIABLES:\nID (unitless)\nIMG_genome_id (unitless)\nGenBank_assembly (unitless)\nSample (unitless)\nCorrected_BioSample (unitless)\nBioProject (unitless)\nrelease_date (unitless)\nlast_updated_date (seconds since 1970-01-01T00:00:00Z)\npublication_date (seconds since 1970-01-01T00:00:00Z)\nDomain (unitless)\nPhylum (unitless)\nClass (unitless)\nOrder (unitless)\nFamily (unitless)\nGenus (unitless)\nSpecies (unitless)\nNCBI_organism_taxid (unitless)\nIsolation_Source (unitless)\nIsolationPlot (unitless)\n... (11 more variables)\n | BCO-DMO | bcodmo_dataset_933610_v1 |