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Dataset Title:	Flow Cytometry : MAPIO
Institution:	MIO UMR 7294 CNRS (Dataset ID: MAP_IO_5591_c48f_b2dc)
Range:	longitude = 31.6856 to 82.3845°E, latitude = -57.992 to -10.6937°N, depth = 6.0 to 6.0m, time = 2021-01-13T12:00:00Z to 2022-03-05T02:00:00Z
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Subset \| Data Access Form \| Files

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Y Axis:

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Constraints

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Server-side Functions

distinct()

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Graph Settings

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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.

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Project {
    String ioos_category "Identifier";
    String long_name "Project";
  }
  Project_starting_Date {
    String ioos_category "Time";
    String long_name "Project.starting.Date";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  Project_ending_Date {
    String ioos_category "Unknown";
    String long_name "Project.ending.Date";
  }
  PI {
    String ioos_category "Identifier";
    String long_name "PI";
  }
  Cytometer_ID {
    String ioos_category "Unknown";
    String long_name "Cytometer.ID";
  }
  Station {
    String ioos_category "Identifier";
    String long_name "Station";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 6, 6;
    String axis "Z";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String ioos_category "Location";
    String long_name "Depth";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range -57.992, -10.6937;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range 31.6856, 82.3845;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  Study_area {
    String ioos_category "Unknown";
    String long_name "Study.area";
  }
  Samples_Operator {
    String ioos_category "Unknown";
    String long_name "Samples.Operator";
  }
  Standards_Reference {
    String ioos_category "Unknown";
    String long_name "Standards.Reference";
  }
  Clustering_Method {
    String ioos_category "Unknown";
    String long_name "Clustering.Method";
  }
  Observation_Type {
    String ioos_category "Unknown";
    String long_name "Observation.Type";
  }
  Platform_Type {
    String ioos_category "Unknown";
    String long_name "Platform.Type";
  }
  Platform_ID {
    String ioos_category "Unknown";
    String long_name "Platform.ID";
  }
  Platform_Nationality {
    String ioos_category "Unknown";
    String long_name "Platform.Nationality";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.6105392e+9, 1.6464456e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Sampling.Date.y";
    String source_name "Sampling.Date.y";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  Analysis_Date {
    String ioos_category "Unknown";
    String long_name "Analysis.Date";
  }
  Standardized_name {
    String ioos_category "Unknown";
    String long_name "Standardized.name";
  }
  Selection_Set {
    String ioos_category "Unknown";
    String long_name "Selection.Set";
  }
  File {
    String ioos_category "Unknown";
    String long_name "File";
  }
  Volume {
    Float64 _FillValue NaN;
    Float64 actual_range 0.156, 6.6761652757989;
    String ioos_category "Unknown";
    String long_name "Volume (ml)";
  }
  Trigger_Channel {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "Trigger.Channel";
  }
  Trigger_level {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "Trigger.level";
  }
  SWS_amplification {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "SWS.amplification";
  }
  FLO_amplification {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "FLO.amplification";
  }
  FLR_amplification {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "FLR.amplification";
  }
  Abundance {
    Float64 actual_range 0.0, 999200.0;
    String ioos_category "Unknown";
    String long_name "Abundance (cell/ml)";
  }
  Mean_Total_FWS {
    Float64 actual_range 0.0, 2.39e+7;
    String ioos_category "Unknown";
    String long_name "Mean.Total.FWS";
  }
  SD_Total_FWS {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 1.634e+7;
    String ioos_category "Unknown";
    String long_name "SD.Total.FWS";
  }
  Mean_Total_SWS {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "Mean.Total.SWS";
  }
  SD_Total_SWS {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "SD.Total.SWS";
  }
  Mean_Total_FLR {
    Float64 actual_range 0.0, 5728000.0;
    String ioos_category "Unknown";
    String long_name "Mean.Total.FLR";
  }
  SD_Total_FLR {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 6234000.0;
    String ioos_category "Unknown";
    String long_name "SD.Total.FLR";
  }
  Mean_Total_FLO {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "Mean.Total.FLO";
  }
  SD_Total_FLO {
    Float32 actual_range -999.99, -999.99;
    String ioos_category "Unknown";
    String long_name "SD.Total.FLO";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Point";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_name "MIO UMR 7294 CNRS";
    String creator_type "institution";
    String creator_url "https://precym.mio.osupytheas.fr/";
    Float64 Easternmost_Easting 82.3845;
    String featureType "Point";
    Float64 geospatial_lat_max -10.6937;
    Float64 geospatial_lat_min -57.992;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 82.3845;
    Float64 geospatial_lon_min 31.6856;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 6.0;
    Float64 geospatial_vertical_min 6.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2023-06-10T07:53:55Z (local files)
2023-06-10T07:53:55Z http://erddap.osupytheas.fr/tabledap/MAP_IO_5591_c48f_b2dc.das";
    String infoUrl "https://precym.mio.osupytheas.fr/";
    String institution "MIO UMR 7294 CNRS";
    String keywords "abundance, analysis.date, Analysis_Date, cell, cell/ml, clustering.method, Clustering_Method, cnrs, cytometer.id, Cytometer_ID, cytometry, data, depth, file, flo.amplification, FLO_amplification, flow, flr.amplification, FLR_amplification, identifier, latitude, longitude, mapio, mean.total.flo, mean.total.flr, mean.total.fws, mean.total.sws, Mean_Total_FLO, Mean_Total_FLR, Mean_Total_FWS, Mean_Total_SWS, mio, observation.type, Observation_Type, platform.id, platform.nationality, platform.type, Platform_ID, Platform_Nationality, Platform_Type, project, project.ending.date, project.starting.date, Project_ending_Date, Project_starting_Date, samples.operator, Samples_Operator, sampling.date.y, sd.total.flo, sd.total.flr, sd.total.fws, sd.total.sws, SD_Total_FLO, SD_Total_FLR, SD_Total_FWS, SD_Total_SWS, selection.set, Selection_Set, standardized.name, Standardized_name, standards.reference, Standards_Reference, station, study.area, Study_area, sws.amplification, SWS_amplification, time, trigger.channel, trigger.level, Trigger_Channel, Trigger_level, umr, volume";
    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 -10.6937;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing -57.992;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String subsetVariables "latitude,longitude,Standardized_name,Selection_Set";
    String summary "The Cytosense automated flow cytometer from the cytobuoy compagny was installed onboard the Marion Dufresnes Sea Water supply, to run semi continuously samples for phytoplankton functional groups resolution.";
    String time_coverage_end "2022-03-05T02:00:00Z";
    String time_coverage_start "2021-01-13T12:00:00Z";
    String title "Flow Cytometry : MAPIO";
    Float64 Westernmost_Easting 31.6856;
  }
}

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 (external link)

Data Access Protocol (DAP) (external link)

and its selection constraints (external link)

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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