ERDDAP > tabledap > Data Access Form

Dataset Title:	NAUTILOS - Microplastic sampler - NIVA
Institution:	NIVA   (Dataset ID: microplastic_sensor_niva)
Information:	Summary | License | Metadata | Background | Files | Make a graph

 

Variable	Optional Constraint #1		Optional Constraint #2		Minimum	Maximum
sample_code_LIMS	!= =~ <= >= = < >		!= =~ <= >= = < >		"NR-2023-13812"	"NR-2023-13839"
station_name	!= =~ <= >= = < >		!= =~ <= >= = < >		"Alta–Honningsvåg"	"Åndalsnes–Træna"
time (UTC)	!= =~ <= >= = < >		!= =~ <= >= = < >		2023-09-11T00:00:00Z	2023-12-15T00:00:00Z
volume (liter)	!= =~ <= >= = < >		!= =~ <= >= = < >		13472.0	98509.0
shape	!= =~ <= >= = < >		!= =~ <= >= = < >		"bundle"	"fragment"
colour_category_EMODnet	!= =~ <= >= = < >		!= =~ <= >= = < >		"BLACK/GRAY"	"YELLOW"
longest_dimension (micron)	!= =~ <= >= = < >		!= =~ <= >= = < >		116.74	4796.91
shortest_dimension (micron)	!= =~ <= >= = < >		!= =~ <= >= = < >		6.35	810.92
size_category	!= =~ <= >= = < >		!= =~ <= >= = < >		"1-5mm"	"300µm-1mm"
polymer_category_AMAP	!= =~ <= >= = < >		!= =~ <= >= = < >		"Abs"	"Rubbers, sealing"
particle_mass (μg)	!= =~ <= >= = < >		!= =~ <= >= = < >		0.0109936056660166	291.908780732108

 

Server-side Functions

distinct()

orderBy orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean orderBySum (" sample_code_LIMS station_name time volume shape colour_category_EMODnet longest_dimension shortest_dimension size_category polymer_category_AMAP particle_mass sample_code_LIMS station_name time volume shape colour_category_EMODnet longest_dimension shortest_dimension size_category polymer_category_AMAP particle_mass sample_code_LIMS station_name time volume shape colour_category_EMODnet longest_dimension shortest_dimension size_category polymer_category_AMAP particle_mass sample_code_LIMS station_name time volume shape colour_category_EMODnet longest_dimension shortest_dimension size_category polymer_category_AMAP particle_mass sample_code_LIMS station_name time volume shape colour_category_EMODnet longest_dimension shortest_dimension size_category polymer_category_AMAP particle_mass ")

File type: (more information) .asc - View OPeNDAP-style ISO-8859-1 comma-separated text. .csv - Download a ISO-8859-1 comma-separated text table (line 1: names; line 2: units; ISO 8601 times). .csvp - Download a ISO-8859-1 .csv file with line 1: name (units). Times are ISO 8601 strings. .csv0 - Download a ISO-8859-1 .csv file without column names or units. Times are ISO 8601 strings. .dataTable - A JSON file formatted for use with the Google Visualization client library (Google Charts). .das - View the dataset's metadata via an ISO-8859-1 OPeNDAP Dataset Attribute Structure (DAS). .dds - View the dataset's structure via an ISO-8859-1 OPeNDAP Dataset Descriptor Structure (DDS). .dods - OPeNDAP clients use this to download the data in the DODS binary format. .esriCsv - Download a ISO_8859_1 .csv file for ESRI's ArcGIS 9.x and below (separate date and time columns). .fgdc - View the dataset's UTF-8 FGDC .xml metadata. .geoJson - Download longitude,latitude,otherColumns data as a UTF-8 GeoJSON .json file. .graph - View a Make A Graph web page. .help - View a web page with a description of tabledap. .html - View an OPeNDAP-style HTML Data Access Form. .htmlTable - View a UTF-8 .html web page with the data in a table. Times are ISO 8601 strings. .iso19115 - View the dataset's ISO 19115-2/19139 UTF-8 .xml metadata. .itx - Download an ISO-8859-1 Igor Text File. Each response column becomes a wave. .json - View a table-like UTF-8 JSON file (missing value = 'null'; times are ISO 8601 strings). .jsonlCSV1 - View a UTF-8 JSON Lines CSV file with column names on line 1 (mv = 'null'; times are ISO 8601 strings). .jsonlCSV - View a UTF-8 JSON Lines CSV file without column names (mv = 'null'; times are ISO 8601 strings). .jsonlKVP - View a UTF-8 JSON Lines file with Key:Value pairs (missing value = 'null'; times are ISO 8601 strings). .mat - Download a MATLAB binary file. .nc - Download a flat, table-like, NetCDF-3 binary file with COARDS/CF/ACDD metadata. .ncHeader - View the UTF-8 header (the metadata) for the NetCDF-3 .nc file. .ncCF - Download a NetCDF-3 CF Discrete Sampling Geometries file (Contiguous Ragged Array). .ncCFHeader - View the UTF-8 header (the metadata) for the .ncCF file. .ncCFMA - Download a NetCDF-3 CF Discrete Sampling Geometries file (Multidimensional Array). .ncCFMAHeader - View the UTF-8 header (the metadata) for the .ncCFMA file. .nccsv - Download a NetCDF-3-like 7-bit ASCII NCCSV .csv file with COARDS/CF/ACDD metadata. .nccsvMetadata - View the dataset's metadata as the top half of a 7-bit ASCII NCCSV .csv file. .ncoJson - Download a UTF-8 NCO lvl=2 JSON file with COARDS/CF/ACDD metadata. .odvTxt - Download longitude,latitude,time,otherColumns as an ISO-8859-1 ODV Generic Spreadsheet File (.txt). .subset - View an HTML form which uses faceted search to simplify picking subsets of the data. .tsv - Download a ISO-8859-1 tab-separated text table (line 1: names; line 2: units; ISO 8601 times). .tsvp - Download a ISO-8859-1 .tsv file with line 1: name (units). Times are ISO 8601 strings. .tsv0 - Download a ISO-8859-1 .tsv file without column names or units. Times are ISO 8601 strings. .wav - Download a .wav audio file. All columns must be numeric and of the same type. .xhtml - View a UTF-8 XHTML (XML) file with the data in a table. Times are ISO 8601 strings. .kml - View a .kml file, suitable for Google Earth. .smallPdf - View a small .pdf image file with a graph or map. .pdf - View a standard, medium-sized .pdf image file with a graph or map. .largePdf - View a large .pdf image file with a graph or map. .smallPng - View a small .png image file with a graph or map. .png - View a standard, medium-sized .png image file with a graph or map. .largePng - View a large .png image file with a graph or map. .transparentPng - View a .png image file (just the data, without axes, landmask, or legend).

(Documentation / Bypass this form )
 
Submit (Please be patient. It may take a while to get the data.)

 

---

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  sample_code_LIMS {
    String long_name "Sample Code LIMS";
  }
  station_name {
    String long_name "Station Name";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.6943904e+9, 1.7025984e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Time";
    String parameter_sdn_name "Elapsed time relative to 1970-01-01T00:00:00Z";
    String parameter_sdn_uri "https://vocab.nerc.ac.uk/collection/P01/current/ELTMEP01/";
    String parameter_sdn_urn "SDN:P01::ELTMEP01";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String unit_sdn_name "Seconds";
    String unit_sdn_run "SDN:P06::UTB";
    String unit_sdn_uri "https://vocab.nerc.ac.uk/collection/P06/current/UTBB/";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  volume {
    Float32 actual_range 13472.0, 98509.0;
    String long_name "Volume";
    String unit_sdn_name "Litres";
    String unit_sdn_run "SDN:P06::ULIT";
    String unit_sdn_uri "https://vocab.nerc.ac.uk/collection/P06/current/ULIT/";
    String units "liter";
    String units_url "https://docs.unidata.ucar.edu/udunits/current/udunits2-accepted.xml";
  }
  shape {
    String long_name "Shape";
  }
  colour_category_EMODnet {
    String long_name "Colour Category EMODnet";
  }
  longest_dimension {
    Float32 actual_range 116.74, 4796.91;
    String source_name "longest_dimension";
    String unit_sdn_name "Micrometres (microns)";
    String unit_sdn_run "SDN:P06::UMIC";
    String unit_sdn_uri "https://vocab.nerc.ac.uk/collection/P06/current/UMIC/";
    String units "micron";
    String units_url "docs.unidata.ucar.edu/udunits/current/udunits2-common.xml";
  }
  shortest_dimension {
    Float32 actual_range 6.35, 810.92;
    String long_name "Shortest Dimension";
    String unit_sdn_name "Micrometres (microns)";
    String unit_sdn_run "SDN:P06::UMIC";
    String unit_sdn_uri "https://vocab.nerc.ac.uk/collection/P06/current/UMIC/";
    String units "micron";
    String units_url "docs.unidata.ucar.edu/udunits/current/udunits2-common.xml";
  }
  size_category {
    String long_name "Size Category";
  }
  polymer_category_AMAP {
    String long_name "Polymer Category AMAP";
  }
  particle_mass {
    Float64 actual_range 0.0109936056660166, 291.908780732108;
    String long_name "Particle Mass";
    String unit_sdn_name "Micrograms";
    String unit_sdn_run "SDN:P06::UGUG";
    String unit_sdn_uri "https://vocab.nerc.ac.uk/collection/P06/current/UGUG/";
    String units "μg";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Other";
    String contributors_email "Bert.vanBavel@niva.no";
    String contributors_name "Bert van Bavel";
    String contributors_orcid "0000-0001-6217-8857";
    String contributors_role "Task leader";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "post@niva.no";
    String creator_name "Norwegian Institute for Water Research";
    String creator_type "Research Organisation";
    String creator_url "person";
    String geospatial_lat_units "degree_east";
    String geospatial_lon_units "degree_north";
    String history 
"2025-06-04T02:30:14Z (local files)
2025-06-04T02:30:14Z https://data-nautilos-h2020.eu/erddap/tabledap/microplastic_sensor_niva.html";
    String infoUrl "https://nautilos-h2020.eu/";
    String inspire "Oceanographic geographical features";
    String institution "NIVA";
    String institution_country "NOR";
    String institution_edmo_code "1417";
    String institution_references "The Norwegian Institute for Water Research (NIVA) is Norways' premier research institute in the fields of water and the environment. They develop science-based knowledge and solutions to challenges related to the interaction between water and climate, the environment, nature, people, resources and society.";
    String keywords "100 um, Earth Science > Oceans > Water Quality > Sea Surface Contaminants > Microplastic Concentration (4E0Ac490-817B-4735-93A3-Ab6775486023), Microplastics, Sampler";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "CC-BY 4.0";
    String naming_authority "NAUTILOS";
    String oceanographic_campaign "Monitoring of microplastics in the Norwegian environment (Mikronor): 2023 and 2024";
    String project_code "NAUTILOS";
    String Project_DOI "https://doi.org/10.3030/101000825";
    String project_edmerp "13720";
    String project_id "101000825";
    String project_name "New Approach to Underwater Technologies for Innovative, Low-cost Ocean obServation";
    String project_statement "This project has received funding from the European Unions' Horizon 2020 research and innovation programme under grant agreement No. 101000825 (NAUTILOS). This output reflects only the authors' view and the European Union cannot be held responsible for any use that may be made of the information contained therein";
    String references "Nautilos Deliverables";
    String sensor_accuracy "Sampler, no sensor accuracy in combination with laboratory microplastic analysis 25%";
    String sensor_last_calibration "100 % recovery microplastics > 300 μm";
    String sensor_nautilos "Microplastic Sampler - NIVA";
    String ship_name "M/S Trollfjord";
    String source "https://www.miljodirektoratet.no/publikasjoner/2024/desember-2024/monitoring-of-microplastics-in-the-norwegian-environment-mikronor-2023/";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "A filter system with a 100 μm mesh size, at a depth of approximately 4-5 m. The FerryBox system enables large volume low cost microplastic sampler consisting of a filter holder allowing large sampling volumes (up to 10 000 L) to enable sampling down to background levels.";
    String time_coverage_end "2023-12-15T00:00:00Z";
    String time_coverage_start "2023-09-11T00:00:00Z";
    String title "NAUTILOS - Microplastic sampler - NIVA";
    String variables "Number of microplastics 100 -1000 μm";
  }
}

 

---

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.

 

---

ERDDAP, Version 2.18
Disclaimers | Privacy Policy | Contact