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Dataset Title:  Spatial surveys of carbonate chemistry conducted in Kaneohe Bay, Hawaii from
small boats during 2015 to 2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_765037)
Range: longitude = -157.83487 to -157.77727°E, latitude = 21.44921 to 21.5138°N, time = 2015-10-31T19:12:00Z to 2017-02-27T00:26:00Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | 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:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  sample {
    Int16 _FillValue 32767;
    Int16 actual_range 1, 151;
    String bcodmo_name "sample";
    String description "Sample bottle number for table reference";
    String long_name "Sample";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  reefstatus {
    String bcodmo_name "site_descrip";
    String description "Reef status is either observed as ‘bleached’ or ‘recovery’ at the time of the survey";
    String long_name "Reefstatus";
    String units "unitless";
  date {
    String bcodmo_name "date";
    String description "Date of the survey in DD-Month-YYYY";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  time2 {
    String bcodmo_name "time";
    String description "Time of survey in local time (GMT -10); 24-hour; formatted as HH:MMM";
    String long_name "Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 21.44921, 21.5138;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude of survey station";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -157.83487, -157.7772667;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude of survey station";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String source_name "long";
    String standard_name "longitude";
    String units "degrees_east";
  temp {
    Float32 _FillValue NaN;
    Float32 actual_range 22.55, 30.4;
    String bcodmo_name "temperature";
    String description "Temperature of seawater";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  sal {
    Float32 _FillValue NaN;
    Float32 actual_range 32.88, 36.7;
    String bcodmo_name "sal";
    String description "Salinity of seawater";
    String long_name "Sal";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "psu";
  ta {
    Float32 _FillValue NaN;
    Float32 actual_range 2018.07, 2313.98;
    String bcodmo_name "TALK";
    String description "Total alkalinity of seawater";
    String long_name "Ta";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MDMAP014/";
    String units "micromoles per kilogram (umol/kg)";
  dic {
    Float32 _FillValue NaN;
    Float32 actual_range 1730.67, 2028.2;
    String bcodmo_name "DIC";
    String description "Dissolved inorganic carbon of seawater";
    String long_name "Dic";
    String units "micromoles per kilogram (umol/kg)";
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.44631872e+9, 1.48815516e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_Local";
    String description "Date and time of survey formatted to ISO8601 standard: yyyy-mm-ddThh:mm-hh:mm";
    String ioos_category "Time";
    String long_name "ISO Date Time Local";
    String source_name "ISO_DateTime_Local";
    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";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Methods description:  
 Total alkalinity (TA) and dissolved inorganic carbon (DIC) spatial surveys
were conducted across the entire K\\u0101ne\\u02bbohe Bay barrier reef flat
including samples offshore from the reef flat boundary on 31 October 2015, 31
June 2016, 12 November 2016, and 26 February 2017. At each station, surface
temperature and salinity were measured with handheld YSI multiprobes and
seawater samples were collected by hand at the surface following standard
protocols for analysis of TA and DIC.
Analytical Methods:  
 Surface seawater samples were collected by hand at ~0.25 m depth using 250
ml Pyrex glass bottles and immediately fixed with 100 \\u00b5L HgCl2 as per
standard protocols (Dickson et al. 2007). Handheld YSI multiprobes (October
2015: YSI 6600 V2; June 2016, November 2016: YSI Professional Plus; February
2017: YSI 556) were calibrated and used to measure temperature and salinity at
the time of sampling. All seawater samples were transported to the Scripps
Coastal and Open Ocean Biogeochemistry lab and analyzed for TA via an open-
cell potentiometric acid titration system developed at Scripps Institution of
Oceanography (SIO) by A. Dickson (Dickson et al. 2007) and DIC via an
automated infra-red inorganic carbon analyzer (AIRICA, Marianda Inc).
Quality Control:  
 Standard protocols were followed for sampling and analysis of seawater TA
and DIC. YSI multiprobes were calibrated prior to each sampling with an
accuracy of \\u00b10.2\\u00b0C for temperature and \\u00b10.3 g kg-1 for
salinity. The mean accuracy (TA\\u00b11.3 \\u00b5mol kg-1, DIC \\u00b11.6
\\u00b5mol kg-1) and precision (TA\\u00b11.3 \\u00b5mol kg-1, DIC \\u00b11.4
\\u00b5mol kg-1) of TA and DIC measurements were evaluated using certified
reference materials (CRM) provided by the laboratory of A. Dickson at SIO and
analyzed every 5 samples for DIC and ~10-15 samples for TA.";
    String awards_0_award_nid "737877";
    String awards_0_award_number "OCE-1255042";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1255042";
    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 
"Spatial surveys of carbonate chemistry in Kaneohe Bay, Hawaii 
  PI: Andreas Andersson (UCSD) 
  Version date: 15-April-2019";
    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 "2019-04-15T19:18:57Z";
    String date_modified "2019-05-07T17:09:54Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.765037.1";
    Float64 Easternmost_Easting -157.7772667;
    Float64 geospatial_lat_max 21.5138;
    Float64 geospatial_lat_min 21.44921;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -157.7772667;
    Float64 geospatial_lon_min -157.83487;
    String geospatial_lon_units "degrees_east";
    String history 
"2022-09-27T14:09:45Z (local files)
2022-09-27T14:09:45Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_765037.das";
    String infoUrl "https://www.bco-dmo.org/dataset/765037";
    String institution "BCO-DMO";
    String instruments_0_acronym "Water Quality Multiprobe";
    String instruments_0_dataset_instrument_description "YSI Handheld Multiparameter Instruments were used to measure in situ temperature (accuracy ± 0.15°C), salinity (accuracy ± 1%), DO_mg (accuracy ± 2%), and DO_% (accuracy ± 2%). Instrument models used: October 2015: YSI 6600 V2; June 2016, November 2016: YSI Professional Plus; February 2017: YSI 556.";
    String instruments_0_dataset_instrument_nid "765039";
    String instruments_0_description "An instrument which measures multiple water quality parameters based on the sensor configuration.";
    String instruments_0_instrument_name "Water Quality Multiprobe";
    String instruments_0_instrument_nid "678";
    String instruments_0_supplied_name "Handheld YSI multi probes";
    String instruments_1_acronym "Automatic titrator";
    String instruments_1_dataset_instrument_description "The open-cell potentiometric acid titration system was developed by the laboratory of A.G. Dickson. Briefly, a known amount of seawater is added to an open cell temperature controlled beaker. Hydrochloric acid is added using a Methrom Dosimat to a pH of 3.5-4.0 and allowed to stabilize to remove CO2 gas formed by the addition of acid. Small aliquots of hydrochloric acid are then added to pH of ~3.0. The titration is monitored by a glass electrode and the total alkalinity of the sample is calculated using a non-linear least-squares method following Dickson et al. (2007).";
    String instruments_1_dataset_instrument_nid "765041";
    String instruments_1_description "Instruments that incrementally add quantified aliquots of a reagent to a sample until the end-point of a chemical reaction is reached.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB12/";
    String instruments_1_instrument_name "Automatic titrator";
    String instruments_1_instrument_nid "682";
    String instruments_1_supplied_name "Open-cell potentiometric acid titration system";
    String instruments_2_dataset_instrument_description "The Automated Infra Red Inorganic Carbon Analyzer (AIRICA) utilizes infrared detection of CO2 gas purged from an acidified seawater sample. A high-precision syringe pump extracts the seawater sample, acidifies the sample with phosphoric acid, and analyzes the gas released with an infrared light analyzer (LICOR). The CO2 signal is integrated for each sample to quantify the total inorganic carbon for a given aliquot of seawater analyzed. Three aliquots and peak integrations are performed for each seawater sample and averaged to determine the dissolved inorganic carbon for each sample. Precision was typically ±1–2 µmol/kg for TA. Please see http://marianda.com/index.php?site=products&subsite=airica for a complete instrument description.";
    String instruments_2_dataset_instrument_nid "765040";
    String instruments_2_description "Instruments measuring carbonate in sediments and inorganic carbon in the water column.";
    String instruments_2_instrument_name "Inorganic Carbon Analyzers";
    String instruments_2_instrument_nid "743387";
    String instruments_2_supplied_name "Automated Infra Red Inorganic Carbon Analyzer (AIRICA)";
    String keywords "altimetry, bco, bco-dmo, biological, chemical, data, dataset, date, dic, dmo, erddap, iso, laboratory, latitude, local, longitude, management, oceanography, office, preliminary, reefstatus, sal, sample, satellite, temperature, time, time2";
    String license "https://www.bco-dmo.org/dataset/765037/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/765037";
    Float64 Northernmost_Northing 21.5138;
    String param_mapping "{'765037': {'lat': 'flag - latitude', 'long': 'flag - longitude', 'ISO_DateTime_Local': 'flag - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/765037/parameters";
    String people_0_affiliation "University of California-San Diego";
    String people_0_affiliation_acronym "UCSD-SIO";
    String people_0_person_name "Andreas Andersson";
    String people_0_person_nid "51444";
    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 "Shannon Rauch";
    String people_1_person_nid "51498";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Nearshore CO2";
    String projects_0_acronym "Nearshore CO2";
    String projects_0_description 
"NSF abstract:
Because of well-known chemical principles, changes in the CO2 chemistry of seawater in the open ocean as a result of rising atmospheric CO2 can be predicted very accurately. On the other hand, in near-shore environments, these projections are much more difficult because the CO2 chemistry is largely modified by biogeochemical processes operating on timescales of hours to months. To make predictions on how near-shore seawater CO2 chemistry will change in response to ocean acidification (OA), it is critical to consider the relative influence of net ecosystem production (NEP) and net ecosystem calcification (NEC), and how these processes might change in response to this major perturbation. Understanding how future OA will alter near-shore seawater CO2 chemistry and variability was identified as a major critical knowledge gap at the recent IPCC WG II/WG I workshop on impacts of ocean acidification on marine biology and ecosystems in January of 2011, and also at the International Ocean Acidification Network workshop in Seattle in June of 2012.
With funding from this CAREER award, a researcher at the Scripps Institute of Oceanography and his students will study how biogeochemical processes and the relative contributions from NEP and NEC modify seawater CO2 chemistry in near-shore environments influenced by different benthic communities under well characterized environmental and physical conditions, and how these processes might change in response to OA. The team will investigate a limited number of contrasting habitats in subtropical (reef crest, back/patch reef, lagoon, seagrass bed, algal mat) and temperate (kelp bed, inter- and sub-tidal, marsh) environments during summer and winter, employing a method that evaluates the function and performance of the carbon cycle of a system using a stoichiometric vector approach based on changes in total dissolved inorganic carbon (DIC) and total alkalinity (TA). These field studies will be complemented by controlled mesocosm experiments with contrasting and mixed benthic communities under different OA scenarios.
The project has two educational components: (1) developing a research-driven OA and biogeochemistry course based on inquiry-, experience-, and collaborative-based learning; and (2) working with the Ocean Discovery Institute (ODI) to engage individuals from a local underrepresented minority community in science through educational activities focused on OA, and also providing a moderate number of internships for high school and college students to engage in this research project.
Broader Impacts: This project will directly support one PhD student, one junior research technician, and two high school and college interns from underrepresented minorities (URM) each summer of the project. It will contribute to the education of 80 undergraduate and graduate students participating in the research based ocean acidification/biogeochemistry course offered four times throughout the duration of the project at SIO/UCSD. Education and curricular material on the topics of OA, including hands-on laboratories, classroom and field-based activities will be developed through the collaboration with the ODI and brought to hundreds of URM students and their teachers in the City Heights area, a community with the highest poverty and ethnic diversity in the San Diego region. This collaboration will enable URM students to directly engage in a rapidly evolving field of research that has high relevance at both the local and global scales. To ensure broad dissemination of this project and the topic of OA, the research team will work with the Google Ocean team to incorporate information and educational material in the Google Ocean Explorer.";
    String projects_0_end_date "2019-05";
    String projects_0_geolocation "San Diego, California; Bermuda; Oahu, Hawaii";
    String projects_0_name "CAREER:   Biogeochemical Modification of Seawater CO2 Chemistry in Near-Shore Environments:   Effect of Ocean Acidification";
    String projects_0_project_nid "737878";
    String projects_0_start_date "2013-06";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 21.44921;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "General study design:\\r\\nIn this study, seawater carbonate chemistry samples were collected across a spatial array of stations across the barrier reef flat of K\\u0101ne\\u02bbohe Bay, Hawai\\u02bbi. The study was designed to assess spatial variability in carbonate chemistry across the barrier reef flat during the 2015 fall coral bleaching event and during a year of recovery following the coral bleaching.";
    String time_coverage_end "2017-02-27T00:26:00Z";
    String time_coverage_start "2015-10-31T19:12:00Z";
    String title "Spatial surveys of carbonate chemistry conducted in Kaneohe Bay, Hawaii from small boats during 2015 to 2017";
    String version "1";
    Float64 Westernmost_Easting -157.83487;
    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.

Tabledap request URLs must be in the form
For example,
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.

ERDDAP, Version 2.02
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