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Dataset Title:  Blue mussel (Mytilus edulis) density data from surveys conducted in coastal
eastern Maine from 2014 to 2017 (MuLTI-2 project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_717620)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | 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 {
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 2014, 2017;
    String bcodmo_name "year";
    String description "Year of sampling";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Sample_Date {
    String bcodmo_name "year";
    String description "Date of sampling in format mm/dd/yy";
    String long_name "Sample Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Sampling_Event {
    String bcodmo_name "event";
    String description "Identifier of sampling event";
    String long_name "Sampling Event";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/EVTAGFL/";
    String units "unitless";
  }
  Station_Code {
    String bcodmo_name "station";
    String description "Station identifier";
    String long_name "Station Code";
    String units "unitless";
  }
  Bed_Designation {
    String bcodmo_name "site";
    String description "Identifier of mussel bed";
    String long_name "Bed Designation";
    String units "unitless";
  }
  Substrate_Type {
    String bcodmo_name "site_descrip";
    String description "Substrate type description.  Habitat of the bed section.";
    String long_name "Substrate Type";
    String units "unitless";
  }
  SubArea {
    Byte _FillValue 127;
    Byte actual_range 1, 15;
    String bcodmo_name "site_descrip";
    String description "Subarea number within mussel bed";
    String long_name "Sub Area";
    String units "unitless";
  }
  Transect {
    Byte _FillValue 127;
    Byte actual_range 1, 4;
    String bcodmo_name "transect";
    String description "Transect number within subarea";
    String long_name "Transect";
    String units "unitless";
  }
  Count {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 5408.0;
    String bcodmo_name "count";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "The number of blue mussels (Mytilus edulis) within a plot (summed across size classes).";
    String long_name "Count";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Sampling and Analytical Methodology:\\u00a0\\u00a0  
 Density of blue mussels (Mytilus edulis) was randomly sampled at each of the
major mussel beds (stations) along the eastern Maine coast from Frenchman Bay
to the Canadian border. Sampling followed a nested design of 4 plots (30 cm x
30 cm) randomly distributed along a 6 meter transect, and three transects
randomly spaced in a random subarea within the greater mussel bed. This
process was repeated for at least three subareas per year per mussel bed.
Where densities of mussels were too great, plots were subset into a 5 x 5
grid, and the average density of 3 randomly chosen grid sections measured, and
the mean extrapolated to the entire area of the plot.
 
\\u00a0";
    String awards_0_award_nid "527111";
    String awards_0_award_number "OCE-1333755";
    String awards_0_data_url "http://nsf.gov/awardsearch/showAward?AWD_ID=1333755";
    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 "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Density 
  PI: P. Yund 
   data version: 2017-10-24";
    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 "2017-10-24T22:04:47Z";
    String date_modified "2020-01-03T19:44:41Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.717620.1";
    String history 
"2020-10-27T18:22:02Z (local files)
2020-10-27T18:22:02Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_717620.das";
    String infoUrl "https://www.bco-dmo.org/dataset/717620";
    String institution "BCO-DMO";
    String keywords "area, bco, bco-dmo, bed, Bed_Designation, biological, chemical, code, count, data, dataset, date, designation, dmo, erddap, event, management, oceanography, office, preliminary, sample, Sample_Date, sampling, Sampling_Event, station, Station_Code, sub, SubArea, substrate, Substrate_Type, transect, type, year";
    String license "https://www.bco-dmo.org/dataset/717620/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/717620";
    String param_mapping "{'717620': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/717620/parameters";
    String people_0_affiliation "Downeast Institute for Applied Marine Research and Education";
    String people_0_affiliation_acronym "DEI";
    String people_0_person_name "Dr Philip O. Yund";
    String people_0_person_nid "51154";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Downeast Institute for Applied Marine Research and Education";
    String people_1_affiliation_acronym "DEI";
    String people_1_person_name "Dr Philip O. Yund";
    String people_1_person_nid "51154";
    String people_1_role "Contact";
    String people_1_role_type "related";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI BCO-DMO";
    String people_2_person_name "Amber York";
    String people_2_person_nid "643627";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "MuLTI-2";
    String projects_0_acronym "MuLTI-2";
    String projects_0_description 
"Acronym \"MuLTI-2\" (Mussel Larval Transport Initiative-2)
Extracted from the NSF award abstract:
Existing larval transport models focus mainly on along-shelf transport and have done little to explicitly incorporate the effects of cross-shelf mixing and transport processes. Yet cross-shelf transits (both outgoing and incoming legs) are critical components of the dispersal paths of coastal invertebrates. This project will explore the role of cross-shelf mixing in the connectivity of blue mussel populations in eastern Maine. Previous work has shown that the Eastern Maine Coastal Current (EMCC) begins to diverge from shore southwest of the Grand Manan Channel and creates a gradient in cross-shelf mixing and larval transport, with cross-shelf mixing being more common on the northeastern end, episodic in the transitional middle area, and then becoming rare in the southwestern half of the region of the Gulf of Maine. As a result, the investigators predict that northeastern populations of mussels are seeded mostly from up-stream sources, while a significant component of self-seeding (local retention) exists in southwestern populations. Larvae settling in the intervening bays are expected to be derived from a mixture of local and up-stream sources. Using a combined empirical and theoretical approach hydrographic, current profile, and larval vertical migration data will be collected and used to develop and validate a high-resolution coastal circulation model coupled to a model of larval behavior. The investigators will model simulations in different years using the empirical data from mussel reproductive output and spawning times. Connectivity predicted from this model will be then tested against independent empirical estimates of connectivity based on trace element fingerprinting for larvae which can be connected to specific natal habitats. Regions of agreement and discrepancy in the model will be identified to guide additional data collection and model refinement. This iterative process will ensure an understanding of both larval transport patterns and processes, and provide estimates of inter-annual variability in connectivity for blue mussel populations in the Gulf of Maine.";
    String projects_0_end_date "2017-08";
    String projects_0_geolocation "Gulf of Maine: Frenchmen Bay (44 28.239 N -68 15.927 W) to Machais Bay (44 39.350 N -67 21.320 W)";
    String projects_0_name "An integrated theoretical and empirical approach to across-shelf mixing and connectivity of mussel populations";
    String projects_0_project_nid "527082";
    String projects_0_start_date "2013-09";
    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 subsetVariables "Bed_Designation";
    String summary "Blue mussel (Mytilus edulis) density data from surveys conducted in coastal eastern Maine from 2014 to 2017.";
    String title "Blue mussel (Mytilus edulis) density data from surveys conducted in coastal eastern Maine from 2014 to 2017 (MuLTI-2 project)";
    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.

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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