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Dataset Title:	[Hydrolytic enzyme activity - Coscinodiscus] - Hydrolytic enzyme activities during CDOM monoculture experiment with Coscinodiscus (Collaborative Research: Planktonic Sources of Chromophoric Dissolved Organic Matter in Seawater)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_748445)
Information:	Summary \| License \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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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 {
  substrate {
    String bcodmo_name "unknown";
    String description "substrate for measuring enzyme activity: a-glu = 4-methylumbelliferyl a-D-glucopyranoside; b-glu = 4-methylumbelliferone (MUF) ß-D-glucopyranoside; leu = L-leucine-4-methylcoumarinyl-7-amide";
    String long_name "Substrate";
    String units "unitless";
  }
  sample {
    String bcodmo_name "sample";
    String description "sample identifier denoted as growth stage (days from start) replicate id";
    String long_name "Sample";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  fluor_t0 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.4, 654.83;
    String bcodmo_name "fluorescence";
    String description "fluorescence intensity at time 0";
    String long_name "Fluor T0";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLPM01/";
    String units "relative fluorescence units";
  }
  fluor_t1 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.83, 1099.15;
    String bcodmo_name "fluorescence";
    String description "fluorescence intensity at time 1";
    String long_name "Fluor T1";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLPM01/";
    String units "relative fluorescence units";
  }
  fluor_t2 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.76, 1746.21;
    String bcodmo_name "fluorescence";
    String description "fluorescence intensity at time 2";
    String long_name "Fluor T2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLPM01/";
    String units "relative fluorescence units";
  }
  fluor_t3 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.16, 2074.2;
    String bcodmo_name "fluorescence";
    String description "fluorescence intensity at time 3";
    String long_name "Fluor T3";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLPM01/";
    String units "relative fluorescence units";
  }
  time_t0 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.01, 0.05;
    String bcodmo_name "time_elapsed";
    String description "time since start of experiment; time point 0";
    String long_name "Time T0";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ELTMZZZZ/";
    String units "hours";
  }
  time_t1 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.04, 3.38;
    String bcodmo_name "time_elapsed";
    String description "time elapsed from start of experiment; time point 1";
    String long_name "Time T1";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ELTMZZZZ/";
    String units "hours";
  }
  time_t2 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.04, 5.83;
    String bcodmo_name "time_elapsed";
    String description "time elapsed from start of experiment; time point 2";
    String long_name "Time T2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ELTMZZZZ/";
    String units "hours";
  }
  time_t3 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.04, 7.93;
    String bcodmo_name "time_elapsed";
    String description "time elapsed from start of experiment; time point 3";
    String long_name "Time T3";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ELTMZZZZ/";
    String units "hours";
  }
  enz_activity_t0 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 801.19;
    String bcodmo_name "unknown";
    String description "enzymatic activity at time 0";
    String long_name "Enz Activity T0";
    String units "nanoMol/hour";
  }
  enz_activity_t1 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.01, 1340.68;
    String bcodmo_name "unknown";
    String description "enzymatic activity at time 1";
    String long_name "Enz Activity T1";
    String units "nanoMol/hour";
  }
  enz_activity_t2 {
    Float32 _FillValue NaN;
    Float32 actual_range -0.01, 2126.34;
    String bcodmo_name "unknown";
    String description "enzymatic activity at time 2";
    String long_name "Enz Activity T2";
    String units "nanoMol/hour";
  }
  enz_activity_t3 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2524.58;
    String bcodmo_name "unknown";
    String description "enzymatic activity at time 3";
    String long_name "Enz Activity T3";
    String units "nanoMol/hour";
  }
  SLOPE {
    Float32 _FillValue NaN;
    Float32 actual_range -0.001, 269.15;
    String bcodmo_name "unknown";
    String description "the slope of the graph of fluorescence intensity vs substrate concentration";
    String long_name "SLOPE";
    String units "unitless";
  }
  RSQR {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 1.0;
    String bcodmo_name "unknown";
    String description "the square of the correlation coefficient of fluorescence intensity vs substrate concentration";
    String long_name "RSQR";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Hydrolytic enzyme activities were determined using
L-leucine-4-methylcoumarinyl-7-amide (MCA) hydrochloride, 4-methylumbelliferyl
\\u03b1-D-glucopyranoside, and 4-methylumbelliferone (MUF)
\\u03b2-D-glucopyranoside (Sigma-Aldrich) as substrate proxies for leucine-
aminopeptidase, \\u03b1-glucosidase, and \\u03b2-glucosidase activities,
respectively. For each bottle and substrate proxy, 196 \\u00b5L of unfiltered
experimental or control water was added in duplicate to a pure-grade black
96-well plate (Brand Life Sciences) containing a single substrate proxy at
saturation levels (final concentration 200 \\u00b5M). Fluorescence (excitation
370 nm, emission 440 nm) was measured in a Tecan Infinite 200 Pro microplate
reader immediately following the addition of the substrate and several more
times over 7-20 h. The well plates were incubated in the dark at in situ
temperature. MUF and MCA standard solutions prepared in seawater were used to
determine hydrolysis rates. Killed controls (boiled sample water) and
ultrapure water samples showed little change over the incubations.";
    String awards_0_award_nid "734588";
    String awards_0_award_number "OCE-1459406";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459406";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Henrietta N Edmonds";
    String awards_0_program_manager_nid "51517";
    String cdm_data_type "Other";
    String comment 
"Coscinodiscus hydrolytic enzyme activities during CDOM monoculture experiment 
   PI: K. Ziervogel (UNH) 
   version: 2018-10-17";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2018-10-18T13:20:58Z";
    String date_modified "2019-03-18T15:43:27Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.748445.1";
    String history 
"2024-11-08T05:53:38Z (local files)
2024-11-08T05:53:38Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_748445.das";
    String infoUrl "https://www.bco-dmo.org/dataset/748445";
    String institution "BCO-DMO";
    String instruments_0_acronym "Flow Cytometer";
    String instruments_0_dataset_instrument_description "Used to make cell counts.";
    String instruments_0_dataset_instrument_nid "748450";
    String instruments_0_description 
"Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells.
(from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm)";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB37/";
    String instruments_0_instrument_name "Flow Cytometer";
    String instruments_0_instrument_nid "660";
    String instruments_0_supplied_name "FACSCalibur flow cytometer (Becton-Dickson)";
    String instruments_1_dataset_instrument_description "Used to measure fluorescence from which hydrolysis rates were calculated.";
    String instruments_1_dataset_instrument_nid "748451";
    String instruments_1_description "Plate readers (also known as microplate readers) are laboratory instruments designed to detect biological, chemical or physical events of samples in microtiter plates. They are widely used in research, drug discovery, bioassay validation, quality control and manufacturing processes in the pharmaceutical and biotechnological industry and academic organizations. Sample reactions can be assayed in 6-1536 well format microtiter plates. The most common microplate format used in academic research laboratories or clinical diagnostic laboratories is 96-well (8 by 12 matrix) with a typical reaction volume between 100 and 200 uL per well. Higher density microplates (384- or 1536-well microplates) are typically used for screening applications, when throughput (number of samples per day processed) and assay cost per sample become critical parameters, with a typical assay volume between 5 and 50 µL per well. Common detection modes for microplate assays are absorbance, fluorescence intensity, luminescence, time-resolved fluorescence, and fluorescence polarization. From: https://en.wikipedia.org/wiki/Plate_reader, 2014-09-0-23.";
    String instruments_1_instrument_name "plate reader";
    String instruments_1_instrument_nid "528693";
    String instruments_1_supplied_name "Tecan Infinite 200 Pro microplate reader";
    String keywords "activity, bco, bco-dmo, biological, chemical, data, dataset, dmo, enz, enz_activity_t0, enz_activity_t1, enz_activity_t2, enz_activity_t3, erddap, fluor, fluor_t0, fluor_t1, fluor_t2, fluor_t3, management, oceanography, office, preliminary, rsqr, sample, slope, substrate, time, time_t0, time_t1, time_t2, time_t3";
    String license "https://www.bco-dmo.org/dataset/748445/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/748445";
    String param_mapping "{'748445': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/748445/parameters";
    String people_0_affiliation "University of New Hampshire";
    String people_0_affiliation_acronym "UNH";
    String people_0_person_name "Kai Ziervogel";
    String people_0_person_nid "734583";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Nancy Copley";
    String people_1_person_nid "50396";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "PlankDOM";
    String projects_0_acronym "PlankDOM";
    String projects_0_description 
"NSF abstract:
Chromophoric dissolved organic matter (CDOM) is a small but important fraction of the marine carbon pool that interacts with solar radiation and thus affects many photochemical and biological processes in the ocean. Despite its importance, the chemical basis for the formation of oceanic CDOM remains unclear. CDOM may be formed from two possible sources: 1) heterotrophic bacterial transformations of primary productivity (plankton-derived), or 2) terrestrially-derived. This project will examine the role of phytoplankton as a source of CDOM in the ocean by utilizing a powerful, new technique to measure particulate organic matter absorbance and fluorescence, discrete chemical measurements of probable precursors to planktonic CDOM, and enzymatic assays. Results of this research will provide new insights into the origin and production of planktonic CDOM and its transformation by heterotrophic bacteria. This research on CDOM will be shared broadly through a module at a North Carolina Aquarium, and streaming live feeds of shipboard activities to elementary school classrooms.
Terrestrial and oceanic dissolved organic matter (DOM) differ in their chemical composition. Laboratory and open-ocean observations suggest that bacterial transformation of phytoplankton DOM produces humic-like CDOM signals that are visually similar to those in terrestrial CDOM. However, prior studies of oceanic CDOM using absorbance and fluorescence fit an electronic interaction (EI) model of intramolecular charge transfer (CT) reactions between donor and acceptor molecules common to partially-oxidized terrestrial molecules found in humic substances. This project will test the hypothesis that phytoplankton and bacteria provide a source of donors and acceptors that are microbially-transformed and linked, enabling CT contacts between them and creating oceanic CDOM. To address this, researchers will systematically study phytoplankton growth, including marine snow formation. A new technique for measuring base-extracted POM (BEPOM) absorbance and fluorescence will be used to incorporate planktonic CDOM results into the EI model, and supplemented with measurements of its probable chemical precursors. These experiments will improve understanding of how the production of CDOM in the ocean is linked to the optics and chemistry of planktonic CDOM formation. Determining the time course and extent of phytoplankton POM and DOM transformation by heterotrophic bacteria during the same phytoplankton growth experiments will provide an in-depth understanding as to how bacterial transformation of marine snow-associated planktonic organic matter drives CDOM production throughout the ocean.";
    String projects_0_end_date "2019-04";
    String projects_0_geolocation "Northern Atlantic Ocean, 34.65 N, 69.63 W";
    String projects_0_name "Collaborative Research: Planktonic Sources of Chromophoric Dissolved Organic Matter in Seawater";
    String projects_0_project_nid "734581";
    String projects_0_start_date "2015-05";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "This dataset is from a laboratory experiment. Four phytoplankton cultures and their associated bacterial communities were incubated in replicate roller bottles (1.9 L) over 3-6 weeks under laboratory conditions. Bacterial dynamics in the culture bottles were measured and correlated with geochemical parameters to determine the role of bacterial activities on the formation of CDOM in the cultures (Kinsey et al., 2018, see below).\\r\\n\\r\\nThe data include fluorescence and bacterial enzyme activity during CDOM Coscinodiscus monoculture experiments. Growth stages were initial and exponential.";
    String title "[Hydrolytic enzyme activity - Coscinodiscus] - Hydrolytic enzyme activities during CDOM monoculture experiment with Coscinodiscus (Collaborative Research: Planktonic Sources of Chromophoric Dissolved Organic Matter in Seawater)";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

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.