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Dataset Title:	[Combined Selfish Bacteria Cell Counts 2023-2024] - Cell counts exhibiting ' Selfish' uptake in the Western North Atlantic, in Danish Coastal Seawater, and Abyssopelagic Waters off the Eastern Coast of Japan under varying hydrostatic pressures, 2023-2024 (Collaborative Research: Pressure effects on microbially- catalyzed organic matter degradation in the deep ocean)
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_963393_v1)
Range:	longitude = -73.0271 to 141.8606°E, latitude = 34.0126 to 55.975°N, depth = 10.0 to 5200.0m, time = 2023-10-11T01:34:03Z to 2024-05-22T15:12:00Z
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Data Access Form | Files

 

Graph Type:  lines linesAndMarkers markers sticks vectors X Axis:  latitude longitude time depth insitu_temp incubation_pressure incubation_temp replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance Y Axis:  latitude longitude time depth insitu_temp incubation_pressure incubation_temp replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance Color:  latitude longitude time depth insitu_temp incubation_pressure incubation_temp replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance   Constraints Optional Constraint #1 Optional Constraint #2 deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance     != =~ <= >= = < >     != =~ <= >= = < >   Server-side Functions  distinct() orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance deployment station latitude longitude time date time_local cast_number depth_description depth insitu_temp sample_type incubation_type incubation_pressure incubation_temp unamended_amended substrate replicate timepoint timepoint_days fields_of_view SB_Abundance Total_Abundance ")   Graph Settings Marker Type:  None Plus X Dot Square Filled Square Circle Filled Circle Up Triangle Filled Up Triangle Borderless Filled Square Borderless Filled Circle Borderless Filled Up Triangle  Size:  3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Color:  Color Bar:  BlackBlueWhite BlackGreenWhite BlackRedWhite BlackWhite BlueWhiteRed BlueWideWhiteRed LightRainbow Ocean OceanDepth Rainbow Rainbow2 Rainfall ReverseRainbow RedWhiteBlue RedWhiteBlue2 RedWideWhiteBlue Spectrum Topography TopographyDepth WhiteBlueBlack WhiteGreenBlack WhiteRedBlack WhiteBlack YellowRed KT_algae KT_amp KT_balance KT_curl KT_deep KT_delta KT_dense KT_gray KT_haline KT_ice KT_matter KT_oxy KT_phase KT_solar KT_speed KT_tempo KT_thermal KT_turbid  Continuity:  Continuous Discrete  Scale:  Linear Log    Minimum:   Maximum:   N Sections:  2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Draw land mask:  under over outline off Y Axis Minimum:   Maximum:    Ascending Descending Linear Log   Redraw the Graph (Please be patient. It may take a while to get the data.)   Optional: Then set the File Type: .asc .csv .csvp .csv0 .dataTable .das .dds .dods .esriCsv .fgdc .geoJson .graph .help .html .htmlTable .iso19115 .itx .json .jsonlCSV1 .jsonlCSV .jsonlKVP .mat .nc .ncHeader .ncCF .ncCFHeader .ncCFMA .ncCFMAHeader .nccsv .nccsvMetadata .ncoJson .odvTxt .subset .tsv .tsvp .tsv0 .wav .xhtml .kml .smallPdf .pdf .largePdf .smallPng .png .largePng .transparentPng (File Type information) and or view the URL: (Documentation / Bypass this form )

Click on the map to specify a new center point. Zoom:  Time range:  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 second(s) minute(s) hour(s) day(s) month(s) year(s)

 

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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

• Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
• Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.
   

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  deployment {
    String long_name "Deployment";
    String units "unitless";
  }
  station {
    String long_name "Station";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 34.0126, 55.975;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude_n";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -73.0271, 141.8606;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude_e";
    String standard_name "longitude";
    String units "degrees_east";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.696988043e+9, 1.71639072e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Iso_datetime_utc";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  date {
    String long_name "Date";
    String units "unitless";
  }
  time_local {
    String long_name "Time_local";
    String units "unitless";
  }
  cast_number {
    String long_name "Cast_number";
    String units "unitless";
  }
  depth_description {
    String long_name "Depth_description";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Int32 actual_range 10, 5200;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth_actual";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  insitu_temp {
    Float32 actual_range 1.5, 26.6;
    String long_name "Insitu_temp";
    String units "degrees Celsius";
  }
  sample_type {
    String long_name "Sample_type";
    String units "unitless";
  }
  incubation_type {
    String long_name "Incubation_type";
    String units "unitless";
  }
  incubation_pressure {
    Float32 actual_range 0.1, 52.0;
    String long_name "Incubation_pressure";
    String units "MPa";
  }
  incubation_temp {
    Int32 actual_range 4, 52;
    String long_name "Incubation_temp";
    String units "degrees Celsius";
  }
  unamended_amended {
    String long_name "Unamended_amended";
    String units "unitless";
  }
  substrate {
    String long_name "Substrate";
    String units "unitless";
  }
  replicate {
    Int32 actual_range 1, 4;
    String long_name "Replicate";
    String units "unitless";
  }
  timepoint {
    Int32 actual_range 0, 2;
    String long_name "Timepoint";
    String units "unitless";
  }
  timepoint_days {
    Int32 actual_range 0, 12;
    String long_name "Timepoint_days";
    String units "days";
  }
  fields_of_view {
    Int32 actual_range 1, 99;
    String long_name "Fields_of_view";
    String units "unitless";
  }
  SB_Abundance {
    Float32 actual_range 0.0, 112000.0;
    String long_name "Sb_abundance";
    String units "Cells mL-1";
  }
  Total_Abundance {
    Float32 actual_range 4980.0, 3020000.0;
    String long_name "Total_abundance";
    String units "Cells mL-1";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Other";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_url "https://www.bco-dmo.org/";
    String defaultDataQuery "&time<now";
    String doi "10.26008/1912/bco-dmo.963393.1";
    Float64 Easternmost_Easting 141.8606;
    Float64 geospatial_lat_max 55.975;
    Float64 geospatial_lat_min 34.0126;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 141.8606;
    Float64 geospatial_lon_min -73.0271;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 5200.0;
    Float64 geospatial_vertical_min 10.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2025-08-02T17:37:53Z (local files)
2025-08-02T17:37:53Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_963393_v1.das";
    String infoUrl "https://osprey.bco-dmo.org/dataset/963393";
    String institution "BCO-DMO";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    Float64 Northernmost_Northing 55.975;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.0126;
    String summary 
"Heterotrophic bacteria and archaea (here: microbes) are critical drivers of the ocean's biogeochemical cycles, active throughout the depth of the ocean. Their capabilities and limitations help determine the rates and locations at which carbon and nutrients are regenerated, as well as the extent to which organic matter is preserved (Hedges 1992). In the deep ocean, at bathy- and abyssopelagic depths (ca. 1000-6000m), these communities are dependent upon the sinking flux of particulate organic matter (POM) from the surface ocean (Bergauer et al. 2018). This dependence means that heterotrophic microbial communities must produce the extracellular enzymes required to solubilize and hydrolyze high molecular weight (HMW) POM to sizes substrates suitable for cellular uptake. A recent global-scale investigation of deep-sea microbes in fact found that the genetic potential for exported (extracellular) enzymes among bacteria in deep waters was far greater than for communities in surface or mesopelagic waters (Zhao et al. 2020). We have new evidence that a substantial fraction of bacteria in bottom water from the North Atlantic Ocean use a specialized set of extracellular enzymes to rapidly take up HMW polysaccharides (Giljan et al. 2022), a substrate processing mechanism that would not be detected with the low molecular weight substrates used in most prior studies of microbial activity in the deep ocean (Nagata et al. 2010).
 
Through our collaboration with the Danish Center for Hadal Research, we were able to use pressurization systems and in situ specialized equipment to investigate the effects of pressures characteristic of bathy- and abyssopelagic depths on microbial communities and their extracellular enzymes in the open North Atlantic Ocean, in Danish Coastal Seawater, and abyssopelagic waters off the Eastern Coast of Japan. 
 
Here we present, in collaboration with colleagues from the Max Planck Institute for Marine Microbiology, the detection and quantification of microbial cells exhibiting selfish uptake behavior of fluorescently-labeled HMW polysaccharides. This dataset includes sample collection metadata, environmental variables, experimental variables, the number of cells detected exhibiting 'selfish' uptake, and total cellular abundance.";
    String time_coverage_end "2024-05-22T15:12:00Z";
    String time_coverage_start "2023-10-11T01:34:03Z";
    String title "[Combined Selfish Bacteria Cell Counts 2023-2024] - Cell counts exhibiting 'Selfish' uptake in the Western North Atlantic, in Danish Coastal Seawater, and Abyssopelagic Waters off the Eastern Coast of Japan under varying hydrostatic pressures, 2023-2024 (Collaborative Research: Pressure effects on microbially-catalyzed organic matter degradation in the deep ocean)";
    Float64 Westernmost_Easting -73.0271;
  }
}

 

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Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP Data Access Protocol (DAP) and its selection constraints.

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

• (easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
• (easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
• (not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

 

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