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    770 MadMacs

    Title
    MadMacs
    Period
    2019-01-01 till 2022-10-31
    Period length
    3 years 9 months 30 days
    Note on interval
    no regular sampling interval
    Description

    Mass development of aquatic macrophytes – causes and consequences of macrophyte removal for ecosystem structure, function, and services

    Keywords
    macrophytes, ecology
    Study site
    _global
    Sampling types
    Phytoplankton, Macrophytes, surface water
    Sampling locations
    Spree, Große Tränke bei Fürstenwalde (IGB-Messstelle 7)
    location
    52.368603, 13.997045
    location
    type
    running water / river (Fluss)
    state
    code
    S64
    description
    Parameters

    chemistry:

    carbon dioxide
    name
    carbon dioxide
    dry mass
    name
    dry mass
    description

    balance after drying

    synonyms
    Trockenmasse
    methan
    name
    methan
    N, ammonia-nitrogen
    name
    N, ammonia-nitrogen
    description

    0,45µm filtrated, photometric determination

    synonyms
    NH4+-N, Ammonium, ammonia, NH4-N, NH4
    nitrous oxide
    name
    nitrous oxide
    N, nitrate-nitrogen
    name
    N, nitrate-nitrogen
    description

    0,45µm filtrated, photometric determination

    synonyms
    NO3-N, NO3--N, nitrate, Nitrat
    N, nitrogen
    name
    N, nitrogen
    description

    determinate with elementar analyzer

    N, total dissolved nitrogen
    name
    N, total dissolved nitrogen
    description

    0,45µm filtrated, nondispersive infrared sensor  (NDIR) after combustion

    synonyms
    Stickstoff gelöst, gesamter gelöster Stickstoff, DN
    N, total nitrogen
    name
    N, total nitrogen
    description

    nondispersive infrared sensor  (NDIR) after combustion

    synonyms
    Gesamtstickstoff
    O, oxygen
    name
    O, oxygen
    P, (soluble reactive phosphorus)
    name
    P, (soluble reactive phosphorus)
    description

    0,45µm filtrated, photometric determination

    synonyms
    anorganischer gelöster Phosphor, ortho-Phosphat-P, DRP, DIP, inorganic soluble phosphorus, phosphate-phosphorus
    P, (total phosphorus)
    name
    P, (total phosphorus)
    description

    photometric determination after digestion

    synonyms
    total phosphorus, gesamter Phosphor

    biology:

    biomass
    name
    biomass
    description

    Plankton biomass

    chlorophyll a
    name
    chlorophyll a
    description

    Attention: There are two different methods:

    HPLC - High Performance Liquid Chromatography (ex situ)

    YSI - Chlorophyll Sensor in Multiparameter probe (in situ)

    synonyms
    Chlorophyllkonzentration, alpha-chlorophyll
    Contact
    Jan Köhler
    Licence for data
    All rights reserved. Please send a request to Jan Köhler if you like to use this data. Mind our data policy: IGB Data Policy
    Project
    MadMacs Project Website
    Current doi
    10.18728/igb-fred-771.0

    Data files (e.g. excel)

    TitlecreatedFiletypeActions
    Data_Kemnader_See.xlsx 11. Nov. 2022 11:31 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Data_Spree.xlsx 11. Nov. 2022 11:31 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Data_Norway.xlsx 11. Nov. 2022 11:31 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Data_South_Africa_Jan_2020.xlsx 11. Nov. 2022 11:31 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.
    Data_France.xlsx 11. Nov. 2022 11:31 datatable: .xlsx
    Error: To access file, please get in touch with the contact person.

    Machine Readable Metadata Files

    FRED provides all metadata of this package in a maschine readable format. There is a pure XML file and one EML file in Ecological Metadata Language. Both files are published under the freeODC-ByLicence.

    • MadMacs.xml
    • MadMacs.eml

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    Parsing data File

    Estimated Time:

    Why does it take so much time?

    While parsing a file, the database has to perform various tasks, some of them needs a lot of CPU and memory for larger files.

    • preprocessing: means automatic detection of headlines, table body, format values or csv-separators
    • copying: means read the file cell by cell and copy all elements to the database. During this format settings can be calculated (for example iso-time)
    • analyzing: check out for different data types (can be time, numeric or text)