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Dataset Title:	[EN638 cell count selfish] - Detection and Quantification of Cells Exhibiting ' Selfish' Uptake in samples taken during R/V Endeavor cruise EN638 in the Western North Atlantic in May 2019 (A mechanistic microbial underpinning for the size-reactivity continuum of dissolved organic carbon degradation)
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_988179_v1)
Range:	longitude = -72.0021 to -53.3949°E, latitude = 34.6369 to 42.83954°N, depth = 2.5 to 5580.0m, time = 2019-05-17T00:20:00Z to 2019-05-25T07:30:00Z
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 timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells Y Axis:  latitude longitude time depth timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells Color:  latitude longitude time depth timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells   Constraints Optional Constraint #1 Optional Constraint #2 deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells     != =~ <= >= = < >     != =~ <= >= = < > deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells     != =~ <= >= = < >     != =~ <= >= = < >   Server-side Functions  distinct() orderBy orderByDescending orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells deployment station latitude longitude time cast_number depth_name depth sample_type unamended_amended substrate timepoint_days timepoint_hours replicate_1_selfish_percent replicate_2_selfish_percent replicate_3_selfish_percent average_selfish_percent standard_deviation_selfish_percent replicate_1_substratecells replicate_2_substratecells replicate_3_substratecells average_substratecells standard_deviation_substratecells replicate_1_cells replicate_2_cells replicate_3_cells average_cells standard_deviation_cells ")   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 34.6369, 42.83954;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -72.0021, -53.3949;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.5580524e+9, 1.5587694e+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";
  }
  cast_number {
    String long_name "Cast_number";
    String units "unitless";
  }
  depth_name {
    String long_name "Depth_name";
    String units "unitless";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float32 actual_range 2.5, 5580.0;
    String axis "Z";
    String ioos_category "Location";
    String long_name "Depth_actual";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  sample_type {
    String long_name "Sample_type";
    String units "unitless";
  }
  unamended_amended {
    String long_name "Unamended_amended";
    String units "unitless";
  }
  substrate {
    String long_name "Substrate";
    String units "unitless";
  }
  timepoint_days {
    Float32 actual_range 0.0, 30.0;
    String long_name "Timepoint_days";
    String units "days";
  }
  timepoint_hours {
    Float32 actual_range 1.0, 720.0;
    String long_name "Timepoint_hours";
    String units "hours";
  }
  replicate_1_selfish_percent {
    Float32 actual_range -14.5, 20.6;
    String long_name "Replicate_1_selfish_percent";
    String units "percent";
  }
  replicate_2_selfish_percent {
    Float32 actual_range -9.0, 30.2;
    String long_name "Replicate_2_selfish_percent";
    String units "percent";
  }
  replicate_3_selfish_percent {
    Float32 actual_range -7.2, 57.2;
    String long_name "Replicate_3_selfish_percent";
    String units "percent";
  }
  average_selfish_percent {
    Float32 actual_range -9.4, 32.8;
    String long_name "Average_selfish_percent";
    String units "percent";
  }
  standard_deviation_selfish_percent {
    Float32 actual_range 0.0, 21.2;
    String long_name "Standard_deviation_selfish_percent";
    String units "percent";
  }
  replicate_1_substratecells {
    Float32 actual_range -35176.0, 198000.0;
    String long_name "Replicate_1_substratecells";
    String units "Cells/ml";
  }
  replicate_2_substratecells {
    Float32 actual_range -36944.0, 327666.0;
    String long_name "Replicate_2_substratecells";
    String units "Cells/ml";
  }
  replicate_3_substratecells {
    Float32 actual_range -35047.0, 353000.0;
    String long_name "Replicate_3_substratecells";
    String units "Cells/ml";
  }
  average_substratecells {
    Float32 actual_range -35024.0, 318359.0;
    String long_name "Average_substratecells";
    String units "Cells/ml";
  }
  standard_deviation_substratecells {
    Float32 actual_range 0.0, 173772.2;
    String long_name "Standard_deviation_substratecells";
    String units "Cells/ml";
  }
  replicate_1_cells {
    Float32 actual_range 11100.0, 3370000.0;
    String long_name "Replicate_1_cells";
    String units "Cells/ml";
  }
  replicate_2_cells {
    Float32 actual_range 0.0, 2570000.0;
    String long_name "Replicate_2_cells";
    String units "Cells/ml";
  }
  replicate_3_cells {
    Float32 actual_range 0.0, 2880000.0;
    String long_name "Replicate_3_cells";
    String units "Cells/ml";
  }
  average_cells {
    Float32 actual_range 11767.0, 2663333.0;
    String long_name "Average_cells";
    String units "Cells/ml";
  }
  standard_deviation_cells {
    Float32 actual_range 0.0, 945797.7;
    String long_name "Standard_deviation_cells";
    String units "Cells/ml";
  }
 }
  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.988179.1";
    Float64 Easternmost_Easting -53.3949;
    Float64 geospatial_lat_max 42.83954;
    Float64 geospatial_lat_min 34.6369;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -53.3949;
    Float64 geospatial_lon_min -72.0021;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 5580.0;
    Float64 geospatial_vertical_min 2.5;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2026-10-05T19:59:04Z (local files)
2026-10-05T19:59:04Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_988179_v1.das";
    String infoUrl "https://osprey.bco-dmo.org/dataset/988179";
    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 42.83954;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.6369;
    String summary 
"Marine dissolved organic carbon (DOC) is one of the largest actively-cycling carbon reservoirs on earth—comparable in magnitude to atmospheric CO2 (Hansell 2013)—and thus is an essential component of the global carbon cycle. DOC has a multitude of sources, including phytoplankton productivity, grazing, excretion, solubilization from particulate organic carbon (POC), viral lysis, and riverine input; the major DOC sink in the ocean is consumption by heterotrophic microbial communities (Carlson & Hansell 2015). Measurement of bulk DOC characteristics such as 14C age and molecular size have demonstrated that the high molecular weight (HMW) DOC fraction is generally younger and more biologically reactive than the low molecular weight (LMW) fraction (e.g. Guo et al. 1996; Walker et al. 2016). We know that a substantial fraction of HMW DOC consists of carbohydrates, including neutral sugars, and that its concentration is lower in the deep ocean than in the upper mesopelagic/surface ocean (Benner & Amon 2015). Characterization of the HMW DOC fraction has primarily used chemical measurements that provide information about monomeric constituents (Benner et al. 1992; Kaiser & Benner 2009), but yield no information on the order in which such constituents are linked together, or about the 3D structure of the intact HMW DOC. Beyond these observations and measurements, however, the specific factors controlling the rate, location, and extent to which DOC is transformed and remineralized by heterotrophic microbial communities in the ocean are still not well understood.
 
A key focus of this project's field work is investigating the potential of marine heterotrophic microbial communities from different water masses and under differing conditions of organic matter availability to hydrolyze six well-characterized high-molecular-weight (HMW) polysaccharides (arabinogalactan, chondroitin sulfate, fucoidan, laminarin, pullulan, and xylan). To better contextualize this hydrolysis, bulk water characterization of the deep chlorophyll maximum (DCM), oxygen minimum zone (OMZ), and bottom waters used in our mesocosm incubation experiments was performed.
 
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 "2019-05-25T07:30:00Z";
    String time_coverage_start "2019-05-17T00:20:00Z";
    String title "[EN638 cell count selfish] - Detection and Quantification of Cells Exhibiting 'Selfish' Uptake in samples taken during R/V Endeavor cruise EN638 in the Western North Atlantic in May 2019 (A mechanistic microbial underpinning for the size-reactivity continuum of dissolved organic carbon degradation)";
    Float64 Westernmost_Easting -72.0021;
  }
}

 

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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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