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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| log in | [Bacterial communities and relative abundances of the pathogen Vibrio coralliilyticus in feces of coral reef fish] - Bacterial communities and relative abundances of the pathogen Vibrio coralliilyticus in feces of coral reef fish collected on the north shore of Mo’orea, French Polynesia, Oct 2020 (CAREER: Testing the effects of predator-derived feces on host symbiont acquisition and health) | Understanding how microbial communities in consumer feces may impact ecosystem health may improve conservation and restoration efforts. To test how microbial communities in fish feces may affect coral reef health, we collected fecal samples from ten fish species, ranging from obligate corallivore to grazer/detritivore. Additionally, samples of corals, algae, sediments, and seawater were collected to test whether bacterial taxa in these samples were also represented in fish feces (N = 5-14 per fish, coral, or algae species/genus). All collections were conducted in October 2020 from the back reef (1-2 m depth) and fore reef (5-10 m depth) in Moorea, between LTER sites 1 and 2 of the Moorea Coral Reef (MCR) Long Term Ecological Research (LTER) site. We conducted bacterial 16S rRNA gene metabarcoding on all samples and found that fecal communities of bacteria differed among fish guilds (obligate corallivores, facultative corallivores, grazer/detritivores). We also used real-time PCR to quantify abundances of Vibrio coralliilyticus, a known coral pathogen, in all fecal samples. Samples were collected and processed, and data were analyzed, by the authors of Grupstra et al., 2023.\n\ncdm_data_type = Other\nVARIABLES:\nsample_name (unitless)\nSRA (unitless)\nBioSample (unitless)\norganism (unitless)\nstrain (unitless)\nisolation_source (unitless)\ncollection_date (unitless)\ngeo_loc_name (unitless)\ndepth_r (Depth, m)\nenv_broad_scale (unitless)\nhost_description (unitless)\nhost_tissue_sampled (unitless)\nhost_diet (unitless)\nhost_feces_dCT (cycles)\nhost_AphiaID (unitless)\nhost_ScientificName (unitless)\nhost_LSID (unitless)\n | BCO-DMO | bcodmo_dataset_935908_v1 | ||||||||||||
| https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_747872.subset | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_747872 | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_747872.graph | https://erddap.bco-dmo.org/erddap/files/bcodmo_dataset_747872/ | public | [Heterosigma akashiwo acclimation] - NCBI accessions of the harmful alga Heterosigma akashiwo (CCMP2393) grown under a range of CO2 concentrations from 200-1000 ppm (Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2) | This dataset includes metadata associated with NCBI BioProject PRJNA377729 \\Impacts of Evolution on the Response of Phytoplankton Populations to Rising CO2\\ PRJNA377729: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA377729. The alga Heterosigma akashiwo was grown at CO2 levels from about 200 to 1000 ppm and then the DNA and RNA were sequenced.\n\ncdm_data_type = Other\nVARIABLES:\nsample_name (unitless)\nsample_title (unitless)\nbioproject_accession (unitless)\norganism (unitless)\nstrain (unitless)\nisolate (unitless)\nhost (unitless)\nisolation_source (unitless)\ntime (Collection Date, seconds since 1970-01-01T00:00:00Z)\ngeo_loc_name (unitless)\nsample_type (unitless)\nbiomaterial_provider (unitless)\ncollected_by (unitless)\ndepth (m)\nenv_biome (unitless)\ngenotype (unitless)\nlat_lon (Latitude, decimal degrees)\npassage_history (unitless)\nsamp_size (unitless)\ntemp_C (degrees Celsius)\nlight_level_umol_m2_s (micromol photons m-2 s-1)\nlight_dark_hr (hours)\nMedia (unitless)\nCO2_ppm (parts per million)\nAlkalinity (micromol per kilogram (umol/kg))\npH (Sea Water Ph Reported On Total Scale, unitless; pH scale)\n | https://erddap.bco-dmo.org/erddap/metadata/iso19115/xml/bcodmo_dataset_747872_iso19115.xml | https://erddap.bco-dmo.org/erddap/info/bcodmo_dataset_747872/index.htmlTable | https://www.bco-dmo.org/dataset/747872
| https://erddap.bco-dmo.org/erddap/rss/bcodmo_dataset_747872.rss | https://erddap.bco-dmo.org/erddap/subscriptions/add.html?datasetID=bcodmo_dataset_747872&showErrors=false&email= | BCO-DMO | bcodmo_dataset_747872 | |||
| https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_658497.subset | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_658497 | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_658497.graph | https://erddap.bco-dmo.org/erddap/files/bcodmo_dataset_658497/ | public | [Isolation culturing and sequencing of bacteria and viruses] - Isolation, culturing, and sequencing of bacteria and viruses collected in Canoe Cove, Nahant, MA during 2010 (Marine Bacterial Viruses project) (How can bacterial viruses succeed in the marine environment?) | Isolation, culturing, and sequencing of bacteria and viruses collected in Canoe Cove, Nahant, MA during 2010 (Marine Bacterial Viruses project)\n\ncdm_data_type = Other\nVARIABLES:\nbioproject_accession (unitless)\nenv_biome (unitless)\ngeo_loc_name (unitless)\norganism_type (unitless)\ncollection_date (unitless)\nisolation_source (unitless)\nsample_name (unitless)\norganism (unitless)\nstrain (unitless)\nisolate (unitless)\nhost (unitless)\nlab_host (unitless)\nsample_type (unitless)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntemp (Temperature, degrees celsius)\nordinal_day_of_isolation (unitless)\ndescription (unitless)\n | https://erddap.bco-dmo.org/erddap/metadata/fgdc/xml/bcodmo_dataset_658497_fgdc.xml | https://erddap.bco-dmo.org/erddap/metadata/iso19115/xml/bcodmo_dataset_658497_iso19115.xml | https://erddap.bco-dmo.org/erddap/info/bcodmo_dataset_658497/index.htmlTable | https://www.bco-dmo.org/dataset/658497
| https://erddap.bco-dmo.org/erddap/rss/bcodmo_dataset_658497.rss | https://erddap.bco-dmo.org/erddap/subscriptions/add.html?datasetID=bcodmo_dataset_658497&showErrors=false&email= | BCO-DMO | bcodmo_dataset_658497 | ||
| https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_906740_v1 | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_906740_v1.graph | https://erddap.bco-dmo.org/erddap/files/bcodmo_dataset_906740_v1/ | public | [Microbiome dynamics of coral reef and cleanerfish] - Microbiome dynamics of coral reef and cleanerfish from ecological surveys, in situ manipulations, and laboratory experiments conducted from 2020-2021 (Collaborative Research: Cleaning stations as hubs for the maintenance and recovery of microbial diversity on coral reefs.) | Coral reefs host some of the most iconic symbiotic interactions in nature and are host to the highest diversity of life on the planet. Cleaning symbiosis, wherein small fish or shrimp remove external parasites and associated microorganisms from specific clients, is common on coral reefs. Sites on the reef occupied by cleaners, or \"cleaning stations\", attract a wide variety of fish species that engage in direct physical contact with the cleaner. In this study, we used a combination of ecological surveys, in situ manipulations, and laboratory experiments to examine the unique features of cleaning stations to understand transfer of bacterial and archaeal symbionts amongst fish and within coral reef environment. We used microbial 16S rRNA gene amplicons, environmental parameters, and other molecular tools to evaluate the dynamics between coral microbiomes, cleanerfish skin microbiomes, and the environment. This dataset contains metadata describing sequenced samples, including sample name, data deposition accession records, and measurements at the time of sample collection.\n\ncdm_data_type = Other\nVARIABLES:\nBioProject_accession (unitless)\nBioSample_accession (unitless)\nsample_name (unitless)\nSRA_accession (unitless)\ncollection_date (unitless)\ngeo_loc_name (unitless)\nhost (unitless)\nlat (degrees_north)\nlongitude (degrees_east)\nisolation_source (unitless)\nhost_common_name (unitless)\nhost_disease (unitless)\nhost_condition (unitless)\nhost_coral_cleaner_goby_pretreatment (unitless)\nhost_coral_reef_id (unitless)\nlocation_survey_date (unitless)\n... (13 more variables)\n | https://erddap.bco-dmo.org/erddap/info/bcodmo_dataset_906740_v1/index.htmlTable | https://www.bco-dmo.org/dataset/906740
| https://erddap.bco-dmo.org/erddap/rss/bcodmo_dataset_906740_v1.rss | https://erddap.bco-dmo.org/erddap/subscriptions/add.html?datasetID=bcodmo_dataset_906740_v1&showErrors=false&email= | BCO-DMO | bcodmo_dataset_906740_v1 | |||||
| log in | [Three-Prime Tag-Sequencing (3' Tag-Seq) Data for Pisaster ochraceus] - Bioproject accession information on tag-sequence data for Pisaster ochraceus samples collected from Bodega Bay, CA, in July 2019 (Collaborative Proposal: Selection and Genetic Succession in the Intertidal -- Population Genomics of Pisaster ochraceus During a Wasting Disease Outbreak and its Aftermath) | Outbreaks of sea star wasting (SSW) have killed millions of sea stars across over 20 taxa in the last decade alone, threatening the health and stability of coastal communities around the world. While the causative agent remains unknown, it has recently been postulated that hypoxia exposure may play a dominant role in the onset of SSW. We leveraged a study that subjected ochre sea stars to organic matter amendment in a controlled laboratory setting to induce hypoxia and used a repeated sampling design to collect non-invasive tissue samples from both healthy and wasting individuals. Following tag-based RNAseq (TagSeq), we analyzed differential gene expression (DGE) patterns among and within these individuals sampled strategically throughout the 15-day experiment. Transcriptional profiles reveal a progressive change in gene expression accompanying the advancement of SSW, reflecting a transition from asymptomatic stars to the onset of characteristic SSW lesions that progressively worsen until, in some cases, the star dies of their symptoms. Included in this dataset is the accession information for 89 individual TagSeq samples across 20 individual Pisaster ochraceus sea stars at multiple time points during the study to make them available for subsequent re-evaluation. The sequence data have been deposited into the NCBI archive under BioProject PRJNA1116313 and will be publicly available on 2025-08-01.\n\ncdm_data_type = Other\nVARIABLES:\nsample_name (unitless)\nbioproject_accession (unitless)\nbioproject_ncbi (unitless)\nassay_type (unitless)\norganism (unitless)\nisolate_id (unitless)\nisolation_source (unitless)\ncollection_date (unitless)\ngeo_loc_name (unitless)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntissue (unitless)\nbiomaterial_provider (unitless)\ncollected_by (unitless)\nhost_tissue_sampled (unitless)\n | BCO-DMO | bcodmo_dataset_934800_v1 | ||||||||||||
| log in | [Whole genome sequence data for Pisaster ochraceus] - Whole genome sequence data for Pisaster ochranceus samples collected from the Pacific coast of North America from July 2004 to May 2018 (Collaborative Proposal: Selection and Genetic Succession in the Intertidal -- Population Genomics of Pisaster ochraceus During a Wasting Disease Outbreak and its Aftermath) | This dataset includes collection and accession information for whole genome sequence (WGS) data from 65 Pisaster ochraceus (ochre sea star) collected across latitudes ranging from SE Alaska to southern California. The sequence data have been deposited into NCBI SRA archive under BioProject PRJNA1117092 and will be publicly available on 2025-08-01. These data are used to evaluate the population genomic diversity and divergence of spatially and environmentally separated populations of Pisaster ochraceus.\n\ncdm_data_type = Other\nVARIABLES:\nsample_name (unitless)\nbioproject_accession (unitless)\nbioproject_ncbi (unitless)\nassay_type (unitless)\norganism (unitless)\nisolation_source (unitless)\ncollection_date (unitless)\niso_collection_date (unitless)\ngeo_loc_name (unitless)\nlatitude (degrees_north)\nlongitude (degrees_east)\ntissue (unitless)\nbiomaterial_provider (unitless)\ncollected_by (unitless)\nhost_tissue_sampled (unitless)\n | BCO-DMO | bcodmo_dataset_934772_v1 | ||||||||||||
| https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_924786_v1 | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_924786_v1.graph | https://erddap.bco-dmo.org/erddap/files/bcodmo_dataset_924786_v1/ | public | [Whole Genome Sequencing of Eelgrass Bodega and Tomales Bay] - Sample collection information and sequence accessions at the National Center for Biotechnology Information (NCBI) for whole genome sequencing of eelgrass (Zostera marina) collected at Bodega and Tomales Bay, CA, USA from July to September 2019 (Using genomics to link traits to ecosystem function in the eelgrass Zostera marina) | This dataset includes sample collection information and sequence accessions at the National Center for Biotechnology Information (NCBI) for whole genome sequencing of eelgrass (Zostera marina) collected at Bodega and Tomales Bay, California, USA from July and September of 2019. Sequence Read Archive (SRA) Experiments and BioSamples can be accessed from the NCBI BioProject PRJNA887384 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA887384/).\n\nResults summary as described in Scheibelhut, et al. (2023): We examine genomic signals of selection in the eelgrass Zostera marina across temperature gradients in adjacent embayments. Although we find many genomic regions with signals of selection within each bay there is very little overlap in signals of selection at the SNP level, despite most polymorphisms being shared across bays. We do find overlap at the gene level, potentially suggesting multiple mutational pathways to the same phenotype. Using polygenic models we find that some sets of candidate SNPs are able to predict temperature across both bays, suggesting that small but parallel shifts in allele frequencies may be missed by independent genome scans. Together, these results highlight the continuous rather than binary nature of parallel evolution in polygenic traits and the complexity of evolutionary predictability.\n\ncdm_data_type = Other\nVARIABLES:\naccession (unitless)\nsample_name (unitless)\nbioproject_accession (unitless)\nSite (unitless)\norganism (unitless)\ncollection_date (unitless)\nisolation_source (unitless)\nlatitude (degrees_north)\nlongitude (degrees_east)\n | https://erddap.bco-dmo.org/erddap/metadata/fgdc/xml/bcodmo_dataset_924786_v1_fgdc.xml | https://erddap.bco-dmo.org/erddap/metadata/iso19115/xml/bcodmo_dataset_924786_v1_iso19115.xml | https://erddap.bco-dmo.org/erddap/info/bcodmo_dataset_924786_v1/index.htmlTable | https://www.bco-dmo.org/dataset/924786
| https://erddap.bco-dmo.org/erddap/rss/bcodmo_dataset_924786_v1.rss | https://erddap.bco-dmo.org/erddap/subscriptions/add.html?datasetID=bcodmo_dataset_924786_v1&showErrors=false&email= | BCO-DMO | bcodmo_dataset_924786_v1 |