The nominal unit labour cost (NULC) index is defined as the ratio of labour cost to labour productivity, where labour cost is the ratio of compensation of employees (current prices) to hours worked by employees, and labour productivity is the ratio of gross domestic product (at market prices in millions, chain-linked volumes reference year 2015) to total hours worked.

The input data are obtained through official transmissions of national accounts' country data in the ESA 2010 transmission programme.

The data are expressed as % change on previous year and as index 2015=100.

Nominal Median and Nominal Mean Income Measures by National Income Definition, Year and Statistic

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Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). Integrated Global Radiosonde Archive is a digital data set archived at the former National Climatic Data Center (NCDC), now National Centers for Environmental Information (NCEI). This dataset contains monthly means of geopotential height, temperature, zonal wind, and meridional wind derived from the Integrated Global Radiosonde Archive (IGRA). IGRA consists of radiosonde and pilot balloon observations at over 1500 globally distributed stations, and monthly means are available for the surface and mandatory levels at many of these stations. The period of record varies from station to station, with many extending from 1970 to 2016. Monthly means are computed separately for the nominal times of 0000 and 1200 UTC, considering data within two hours of each nominal time. A mean is provided, along with the number of values used to calculate it, whenever there are at least 10 values for a particular station, month, nominal time, and level.

Cross sectional data, all countries for the statistic Nominal_Exchange_Rate_3_Year_Change_In_Percent. Indicator Definition:Nominal Exchange Rate 1 Year Change In Percent. The Exchange Rate is defined according to the Quantity Notation, that is, foreign currency (here always the USD) per domestic currency (for example the euro for Germany). Hence, a higher value means, that the domestic currency appreciated as more foreign currency units can be purchased for one unit of domestic currency.Indicator Unit:The statistic is measured in Percent.Descriptive Statistics regarding the Indicator "Nominal Exchange Rate 3 Year Change In Percent":The number of countries with data stands at: 153 countries.The average value across those countries stands at: -4.50.The standard deviation across those countries stands at: 25.54.The lowest value stands at: -98.32, and was observed in Lebanon (LBP), which in this case constitutes the country that ranks last.The highest value stands at: 36.51, and was observed in Albania (ALL), which in this case constitutes the country that ranks first.Looking at countries with values, the top 5 countries are:1. Albania, actual value 36.51, actual ranking 1.2. Costa Rica, actual value 36.30, actual ranking 2.3. Afghanistan, actual value 24.75, actual ranking 3.4. Poland, actual value 23.95, actual ranking 4.5. Sri Lanka, actual value 20.48, actual ranking 5.Looking at countries with values, the bottom 5 countries are:1. Lebanon, actual value -98.32, actual ranking 153.2. Venezuela, RB, actual value -94.88, actual ranking 152.3. Iran, Islamic Rep., actual value -93.01, actual ranking 151.4. Argentina, actual value -89.58, actual ranking 150.5. South Sudan, actual value -89.00, actual ranking 149.

This is a no-arbitrage dynamic term structure model, implemented as in Kim and Wright using the methodology of Kim and Orphanides . The underlying model is the standard affine Gaussian model with three factors that are latent (i.e., the factors are defined only statistically and do not have a specific economic meaning). The model is parameterized in a maximally flexible way (i.e., it is the most general model of its kind with three factors that are econometrically identified). In the estimation of the parameters of the model, data on survey forecasts of 3-month Treasury bill (T-bill) rate are used in addition to yields data in order to help address the small sample problems that often pervade econometric estimation with persistent time series like bond yields.

Context

This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.

Content

Attribute Information:

InvoiceNo:

Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.

StockCode:

Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product.

Description:

Product (item) name. Nominal.

Quantity:

The quantities of each product (item) per transaction. Numeric.

InvoiceDate:

Invice date and time. Numeric. The day and time when a transaction was generated.

UnitPrice:

Unit price. Numeric. Product price per unit in sterling .

CustomerID:

Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer.

Country:

Country name. Nominal. The name of the country where a customer resides.

Acknowledgements

Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

TSIS-1 SIM Solar Spectral Irradiance V09 ; ; SELECTION CRITERIA ; date range: 20180314 to 20230129 ; cadence: 24 hours ; spectral range: 200.0 to 2400.0 nm ; number of data: 3307488 ; identifier_product_doi: 10.5067/TSIS/SIM/DATA318 ; identifier_product_doi_authority: http://dx.doi.org/ ; END SELECTION CRITERIA ;
; DATA DEFINITIONS, number = 11 (name, type, format) ; nominal_date_yyyymmdd, R8, f11.2 ; nominal_date_jdn, R8, f11.2 ; wavelength, R4, f9.3 (nm) ; instrument_mode_id, I2, i3 ; data_version, I2, i3 ; irradiance_1AU, R8, e15.8 (W/m^2/nm) ; instrument_uncertainty, R8, e15.8 (W/m^2/nm, 1 sigma) ; measurement_precision, R8, e15.8 (W/m^2/nm, 1 sigma) ; measurement_stability, R8, e15.8 (W/m^2/nm, 1 sigma) ; additional_uncertainty, R8, e15.8 (W/m^2/nm, 1 sigma) ; quality, UI2, i6 ; END DATA DEFINITIONS ; ; Background on the Total and Spectral Solar Irradiance Sensor (TSIS-1) ; ; The Total and Spectral Solar Irradiance Sensor (TSIS-1) level 3 (L3) data product is constructed ; using measurements from the Total Irradiance Monitor (TIM) and Spectral Irradiance Monitor (SIM) ; instruments. The TIM instrument measures the total solar irradiance (TSI) that is incident at the ; outer boundaries of the atmosphere and the SIM instrument measures the solar spectral irradiance ; (SSI) from 200 nm to 2400 nm, which are combined into 12-hr and 24-hr solar spectra. The TSIS-1 data ; products are provided on a fixed wavelength scale, which has a variable resolution over the ; spectral range. Irradiances are reported at a mean solar distance of 1 AU and zero relative line-of- ; sight velocity with respect to the Sun. ; ; Table: Solar Spectral Irradiance (SSI) Measurement Summary. ; ; Measuring Instrument SIM ; Temporal Cadence Daily ; Detector Diodes (200 nm to 1620 nm), ESR (1620 nm to 2400 nm) ; Instrument Modes 86 (UV), 85 (VIS), 84 (IR), 83 (ESR) ; Spectral Range 200 nm to 2400 nm ; ; The spectral irradiances are tabulated below ("DATA RECORDS"), with each row giving the nominal date ; (YYYYMMDD.D), nominal date (Julian Day), wavelength center (nm), instrument mode, data version, ; spectral irradiance @ 1au (irradiance_1AU, Watts/m^2/nm), instrument_uncertainty (Watts/m^2/nm), ; measurement_precision (Watts/m^2/nm), measurement_stability (Watts/m^2/nm), additional_uncertainty ; (Watts/m^2/nm), and a "quality" (data quality flag) value. Measurement_stability is given as ; 0.00000000e+00 (0.0) at wavelengths > 1050 nm, where we do not currently calculate a degradation ; correction, and for all data that arrives after the bi-annual Channel C calibration scans. The ; bi-annual Channel C scans trigger a new data release version, so there could be up to six months of ; measurement stability values that are 0.0 until determined during the creation of the next data release. ; Data quality flags are assigned to each spectral measurement in the 'quality' column. The value in this ; column is the addition of all the bit-wise data quality flags (DQF) associated with a given measurement. ; Nominal data has a DQF of '0'. The L3 TSIS-1 SIM data quality flags are: ; ; VALUE CONDITION ; ----- --------- ; 1 Missing data ; 2 Backfilled data (from previous day) ; 512 Data taken with offset pointing; a spectral correction has been applied ; ; Data with the '512' bit set was taken from March 19, 2022 through May 19, 2022. During this period, ; the TSIS-1 SIM pointing was off by ~1 arcmin due to external contamination of the pointing system ; quad-diode (HFSSB). A wavelength-dependent correction has been applied to data during this period, ; and the corresponding additional irradiance uncertainties associated with this correction are given ; in the 'additional_uncertainty' column. Note that it is possible that multiple flags can be set on ; the same measurement. For example, a quality of '514' is backfilled data, and the data used was taken ; during the offset pointing. ; ; Instrument_uncertainty, measurement_precision, measurement_stability, and additional_uncertainty are ; all in units of (Watts/m^2/nm). ; ; Each field (column) is defined and further described in the "DATA DEFINITIONS" section. ; ; An IDL file reader (http://lasp.colorado.edu/data/tsis/file_readers/read_lasp_ascii_file.pro) is ; available which will read this file and return an array of structures whose field names and types ; are taken from the "DATA DEFINITIONS" section. ; ; Erik Richard (2023), Level 3 (L3) Solar Spectral Irradiance Daily Means V009, ; Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), ; Accessed [Data Access Date] at http://dx.doi.org/10.5067/TSIS/SIM/DATA318 ; ; For more information on the TSIS-1 instruments and data products, see: ; http://lasp.colorado...

United States FRBOP Forecast: Nominal GDP: saar: Mean data was reported at 21,066.085 USD bn in Mar 2019. This records an increase from the previous number of 20,903.829 USD bn for Dec 2018. United States FRBOP Forecast: Nominal GDP: saar: Mean data is updated quarterly, averaging 6,548.645 USD bn from Dec 1968 (Median) to Mar 2019, with 202 observations. The data reached an all-time high of 21,066.085 USD bn in Mar 2019 and a record low of 884.782 USD bn in Dec 1968. United States FRBOP Forecast: Nominal GDP: saar: Mean data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.A003: NIPA 2018: GDP by Expenditure: Current Price: saar: Forecast: Federal Reserve Bank of Philadelphia.

SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).

Nominale Median- und Nominalmitteleinkommensmassnahmen

These data represent daily values (daily mean, instantaneous daily output, diurnal cycle) of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The 20th century simulation (included year 2000) was initiated from year 111 of the control preindustrial simulation (nominal year 1970), when equilibrium was reached (corresponds to nominal year 1860 of 20C3M). Forcing agents included: CO2,CH4,N2O,O3,CFC11(including other CFCs and HFCs),CFC12; sulfate(Boucher),BC,sea salt,desert dust aerosols. This is followed by a commitment experiment for the 21th century (year 2001-2100) with all concentrations fixed at their levels of year 2000.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRA2: SRES A2) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRA2_1_MM_hur850

The time coverage of the experiment is 1/1/1860 - 31/12/2000 , but for relative_humidity (hur) and all variables on level 925hPa the storage begins only at 1/1/1900 (Only for run1, run2 is complete).

For this experiment 2 ensemble runs were carried out.

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

These data represent monthly averaged values (monthly mean (MM) and diurnal cycle (DC)) of selected variables for ENSEMBLES (http://www.ensembles-eu.org). The list of output variables can be found in: http://ensembles.wdc-climate.de/output-variables

The 1PCTTO4X simulation (included year 2150) was initiated from nominal year 1970 of preindustriel run,when equilibrium was reached (corresponds to nominal year 1860 of CO2-quadrupling experiment). Forcing agents included: CO2, CH4, N2O, O3, CFC11 (including other CFCs and HFCs), CFC12; sulfate(Boucher), BC, sea salt, desert dust aerosols.

These datasets are available in netCDF format. The dataset names are composed of - centre/model acronym (e.g. CNCM3: CNRM/CM3) - scenario acronym (e.g. SRA1B: SRES A1B) - run number (e.g. 1: run 1) - time interval (MM:monthly mean, DM:daily mean, DC:diurnal cycle, 6H:6 hourly, 12h:12hourly) - variable acronym with level value --> example: CNCM3_SRA1B_1_MM_hur850

Technical data to this experiment: CNRM-CM3 (2004): atmosphere: Arpege-Climat v3 (T42L45, cy 22b+); ocean: OPA8.1; sea ice: Gelato 3.10; river routing: TRIP

Data are reported as mean (SD). F and T values are reported for independent T tests for means and chi-square values (χ2) for nominal data. An asterisk denotes significant p values. N = number.

1 Introduction

The Peatland Decomposition Database (PDD) stores data from published litterbag experiments related to peatlands. Currently, the database focuses on northern peatlands and Sphagnum litter and peat, but it also contains data from some vascular plant litterbag experiments. Currently, the database contains entries from 34 studies, 2,160 litterbag experiments, and 7,297 individual samples with 117,841 measurements for various attributes (e.g. relative mass remaining, N content, holocellulose content, mesh size). The aim is to provide a harmonized data source that can be useful to re-analyse existing data and to plan future litterbag experiments.

The Peatland Productivity and Decomposition Parameter Database (PPDPD) (Bona et al. 2018) is similar to the Peatland Decomposition Database (PDD) in that both contain data from peatland litterbag experiments. The differences are that both databases partly contain different data, that PPDPD additionally contains information on vegetation productivity, which PDD does not, and that PDD provides more information and metadata on litterbag experiments, and also measurement errors.

2 Updates

Compared to version 1.0.0, this version has a new structure for table experimental_design_format, contains additional metadata on the experimental design (these were omitted in version 1.0.0), and contains the scripts that were used to import the data into the database.

3 Methods

3.1 Data collection

Data for the database was collected from published litterbag studies, by extracting published data from figures, tables, or other data sources, and by contacting the authors of the studies to obtain raw data. All data processing was done with R (R version 4.2.0 (2022-04-22)) (R Core Team 2022).

Studies were identified via a Scopus search with search string (TITLE-ABS-KEY ( peat* AND ( "litter bag" OR "decomposition rate" OR "decay rate" OR "mass loss")) AND NOT ("tropic*")) (2022-12-17). These studies were further screened to exclude those which do not contain litterbag data or which recycle data from other studies that have already been considered. Additional studies with litterbag experiments in northern peatlands we were aware of, but which were not identified in the literature search were added to the list of publications. For studies not older than 10 years, authors were contacted to obtain raw data, however this was successful only in few cases. To date, the database focuses on Sphagnum litterbag experiments and not from all studies that were identified by the literature search data have been included yet in the database.

Data from figures were extracted using the package ‘metaDigitise’ (1.0.1) (Pick, Nakagawa, and Noble 2018). Data from tables were extracted manually.

Data from the following studies are currently included: Farrish and Grigal (1985), Bartsch and Moore (1985), Farrish and Grigal (1988), Vitt (1990), Hogg, Lieffers, and Wein (1992), Sanger, Billett, and Cresser (1994), Hiroki and Watanabe (1996), Szumigalski and Bayley (1996), Prevost, Belleau, and Plamondon (1997), Arp, Cooper, and Stednick (1999), Robbert A. Scheffer and Aerts (2000), R. A. Scheffer, Van Logtestijn, and Verhoeven (2001), Limpens and Berendse (2003), Waddington, Rochefort, and Campeau (2003), Asada, Warner, and Banner (2004), Thormann, Bayley, and Currah (2001), Trinder, Johnson, and Artz (2008), Breeuwer et al. (2008), Trinder, Johnson, and Artz (2009), Bragazza and Iacumin (2009), Hoorens, Stroetenga, and Aerts (2010), Straková et al. (2010), Straková et al. (2012), Orwin and Ostle (2012), Lieffers (1988), Manninen et al. (2016), Johnson and Damman (1991), Bengtsson, Rydin, and Hájek (2018a), Bengtsson, Rydin, and Hájek (2018b), Asada and Warner (2005), Bengtsson, Granath, and Rydin (2017), Bengtsson, Granath, and Rydin (2016), Hagemann and Moroni (2015), Hagemann and Moroni (2016), B. Piatkowski et al. (2021), B. T. Piatkowski et al. (2021), Mäkilä et al. (2018), Golovatskaya and Nikonova (2017), Golovatskaya and Nikonova (2017).

4 Database records

The database is a ‘MariaDB’ database and the database schema was designed to store data and metadata following the Ecological Metadata Language (EML) (Jones et al. 2019). Descriptions of the tables are shown in Tab. 1.

The database contains general metadata relevant for litterbag experiments (e.g., geographical, temporal, and taxonomic coverage, mesh sizes, experimental design). However, it does not contain a detailed description of sample handling, sample preprocessing methods, site descriptions, because there currently are no discipline-specific metadata and reporting standards. Table 1: Description of the individual tables in the database.

Name Description

attributes Defines the attributes of the database and the values in column attribute_name in table data.

citations Stores bibtex entries for references and data sources.

citations_to_datasets Links entries in table citations with entries in table datasets.

custom_units Stores custom units.

data Stores measured values for samples, for example remaining masses.

datasets Lists the individual datasets.

experimental_design_format Stores information on the experimental design of litterbag experiments.

measurement_scales, measurement_scales_date_time, measurement_scales_interval, measurement_scales_nominal, measurement_scales_ordinal, measurement_scales_ratio Defines data value types.

missing_value_codes Defines how missing values are encoded.

samples Stores information on individual samples.

samples_to_samples Links samples to other samples, for example litter samples collected in the field to litter samples collected during the incubation of the litterbags.

units, unit_types Stores information on measurement units.

5 Attributes Table 2: Definition of attributes in the Peatland Decomposition Database and entries in the column attribute_name in table data.

Name Definition Example value Unit Measurement scale Number type Minimum value Maximum value String format

4_hydroxyacetophenone_mass_absolute A numeric value representing the content of 4-hydroxyacetophenone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

4_hydroxyacetophenone_mass_relative_mass A numeric value representing the content of 4-hydroxyacetophenone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

4_hydroxybenzaldehyde_mass_absolute A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

4_hydroxybenzaldehyde_mass_relative_mass A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

4_hydroxybenzoic_acid_mass_absolute A numeric value representing the content of 4-hydroxybenzoic acid, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

4_hydroxybenzoic_acid_mass_relative_mass A numeric value representing the content of 4-hydroxybenzoic acid, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

abbreviation In table custom_units: A string representing an abbreviation for the custom unit. gC NA nominal NA NA NA NA

acetone_extractives_mass_absolute A numeric value representing the content of acetone extractives, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

acetone_extractives_mass_relative_mass A numeric value representing the content of acetone extractives, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

acetosyringone_mass_absolute A numeric value representing the content of acetosyringone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

acetosyringone_mass_relative_mass A numeric value representing the content of acetosyringone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

acetovanillone_mass_absolute A numeric value representing the content of acetovanillone, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

acetovanillone_mass_relative_mass A numeric value representing the content of acetovanillone, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

arabinose_mass_absolute A numeric value representing the content of arabinose, as described in Straková et al. (2010). 0.26 g ratio real 0 Inf NA

arabinose_mass_relative_mass A numeric value representing the content of arabinose, as described in Straková et al. (2010). 0.26 g/g ratio real 0 1 NA

ash_mass_absolute A numeric value representing the content of ash (after burning at 550°C). 4 g ratio real 0 Inf NA

ash_mass_relative_mass A numeric value representing the content of ash (after burning at 550°C). 0.05 g/g ratio real 0 Inf NA

attribute_definition A free text field with a textual description of the meaning of attributes in the dpeatdecomposition database. NA NA nominal NA NA NA NA

attribute_name A string describing the names of the attributes in all tables of the dpeatdecomposition database. attribute_name NA nominal NA NA NA NA

bibtex A string representing the bibtex code used for a literature reference throughout the dpeatdecomposition database. Galka.2021 NA nominal NA NA NA NA

bounds_maximum A numeric value representing the minimum possible value for a numeric attribute. 0 NA interval real Inf Inf NA

bounds_minimum A numeric value representing the maximum possible value for a numeric attribute. INF NA interval real Inf Inf NA

bulk_density A numeric value representing the bulk density of the sample [g cm-3]. 0,2 g/cm^3 ratio real 0 Inf NA

C_absolute The absolute mass of C in the sample. 1 g ratio real 0 Inf NA

C_relative_mass The absolute mass of C in the sample. 1 g/g ratio real 0 Inf NA

C_to_N A numeric value representing the C to N ratio of the sample. 35 g/g ratio real 0 Inf NA

C_to_P A numeric value representing the C to P ratio of the sample. 35 g/g ratio real 0 Inf NA

Ca_absolute The