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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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.

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...

Overview This public data was provided by Divvy, a Chicago-based bike share company through the Google Data Analytics Professional Certificate at Coursera. It includes data between August 2022 and July 2023. The Divvy Data License Agreement can be found here. The following CSV files are found in this dataset:

• 202208-divvy-tripdata.csv
• 202209-divvy-oublictripdata.csv
• 20220-divvy-tripdata.csv
• 202211-divvy-tripdata.csv
• 202212-divvy-tripdata.csv
• 202301-divvy-tripdata.csv
• 202302-divvy-tripdata.csv
• 202303-divvy-tripdata.csv
• 202304-divvy-tripdata.csv
• 202305-divvy-tripdata.csv
• 202306-divvy-tripdata.csv
• 202307-divvy-tripdata.csv

Data Dictionary

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.

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.

SORCE Spectral Irradiance ; ; SELECTION CRITERIA ; date range: 20030414 to 20200225 ; cadence: 24 hours (daily) ; spectral range: 240.0 to 2413.0 nm ; number of data: 7142777 ; identifier_product_doi: 10.5067/LDDKZ3PXZZ5G ; identifier_product_doi_authority: https://doi.org/ ; END SELECTION CRITERIA ; ; DATA DEFINITIONS, number = 9 (name, type, format) ; nominal_date_yyyymmdd, R8, f10.1 ; nominal_date_jdn, R8, f10.1 ; min_wavelength, R4, f8.2 (nm) ; max_wavelength, R4, f8.2 (nm) ; instrument_mode_id, I2, i3 ; data_version, I2, i3 ; irradiance, R8, e13.6 (W/m^2/nm) ; irradiance_uncertainty, R4, e11.4 (W/m^2/nm, 1 sigma) ; quality, R4, f8.1 (see release notes for description) ; END DATA DEFINITIONS ; ; Background on the SORCE-SIM Spectral Irradiance Measurements ; ; The SORCE-SIM Solar Spectral Irradiance (SSI) data products are provided on a ; fixed wavelength scale which varies in spectral resolution from 1-34 nm over the ; entire 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. ; ; As a separate data product, a composite SORCE SSI using the XPS, SOLSTICE, and ; SIM instruments, covering 0.1-2412.3 nm, is also delivered daily to ; http://lasp.colorado.edu/lisird/data/sorce_ssi_l3/. ; ; Table: SORCE-SIM Solar Spectral Irradiance (SSI) Measurement Summary. ; ; Measuring Instrument SIM (Spectral Irradiance Monitor) ; Temporal Cadence Daily ; Detector Radiometer (ESR) and Photodiodes (UV, VIS, & IR) ; Instrument Modes 31 (ESR), 41 (VIS), 43 (UV), 44 (IR) ; Spectral Range 240-2412.3 nm ; Spectral Resolution variable (1-34 nm) ; Accuracy 2% ; Long-Term Repeatability < 0.1%/yr ; ; Data QUALITY and IRRADIANCE UNCERTAINTY are reported in V27. MISSING data have ; values of 0.0000e+00 for both IRRADIANCE and IRRADIANCE UNCERTAINTY. UV data ; before mission day 800 (yyyymmdd = 20050403) in the 306-310 nm bandpass are ; treated as MISSING due to potential saturation. All IR (950-1600 nm) IRRADIANCE ; UNCERTAINTY values are set to 2.5000e-04. See the SORCE-SIM V27 release notes ; for justification and further details. ; ; Solar spectral irradiances are tabulated below ("DATA RECORDS"), with each row ; giving the nominal date in YYYYMMDD.D and Julian Day Number (JDN), the ; wavelength of the irradiance measurement (repeated in the MIN_WAVELENGTH and ; MAX_WAVELENGTH fields), the INSTRUMENT_MODE, DATA_VERSION, spectral IRRADIANCE, ; IRRADIANCE_UNCERTAINTY, and data QUALITY flag. Each field (column) is defined ; and described in the "DATA DEFINITIONS" above. An IDL file reader ; (http://lasp.colorado.edu/data/sorce/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. ; ; Jerald Harder (2020), SORCE SIM Level 3 Solar Spectral Irradiance Daily Means V027, ; Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center ; (GES DISC), Accessed [Data Access Date] at https://doi.org/10.5067/LDDKZ3PXZZ5G ; ; This data file, release notes, and other SORCE data products may be obtained ; from: http://lasp.colorado.edu/home/sorce/data/ ; ; For more information on the SORCE instruments and data products, see: ; http://lasp.colorado.edu/home/sorce/

Foto de Dawid Zawiła en Unsplash

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Finland. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 107.03 as of 06/30/2025. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -1.37 percent compared to the value the year prior.The 1 year change in percent is -1.37.The 3 year change in percent is -11.45.The 5 year change in percent is -4.65.The 10 year change in percent is 0.0996.The Serie's long term average value is 61.26. It's latest available value, on 06/30/2025, is 74.70 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 03/31/1970, to it's latest available value, on 06/30/2025, is +1,486.88%.The Serie's change in percent from it's maximum value, on 06/30/2022, to it's latest available value, on 06/30/2025, is -11.45%.

Data Set Overview The Mars Express (MEX) Planetary Fourier Spectrometer (PFS) Data Archive is a collection of raw data collected during the MEX Mission to Mars. For more information on the investigations proposed see the PFS documentations in the DOCUMENT/ folder. This data set was collected during the MEX Mission phases: First Extension Mission Phase Mission Phase Definition It should be noted that the Mars Express (MEX) Planetary Fourier Spectrometer (PFS) group uses mission phases which deviate from the ones defined in the MISSION.CAT files given by ESA in order to keep the keywords and abbreviations consistent for Mars Express, Venus Express and Rosetta. Those mission phase abbreviations are also used in the data description field of the dataset_id. MaRS mission name | abbreviation | time span Near Earth Verification | NEV | 20030602 20030731 Interplanetary Cruise | IC | 20030801 20031225 Nominal Mission | Nominal | 20031226 20051130 First Extension Mission | EXT1 | 20060101 20070930 Second Extension Mission| EXT2 | 20071001 20091231 Data files Data files are: The tracking files from Deep Space Network (DSN) and from the Intermediate Frequency Modulation System (IFMS) used by the ESA ground station New Norcia. Level 1b data are archived. The Geometry files All Level binary data files will have the file name extension eee .DAT Data levels It should be noted that these data levels which are also used in the file names and data directories are PSA dat truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

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).

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Greece. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 115.20 as of 6/30/2025, the highest value at least since 6/30/1997, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.31 percent compared to the value the year prior.The 1 year change in percent is 7.31.The 3 year change in percent is 35.27.The 5 year change in percent is 60.29.The 10 year change in percent is 75.66.The Serie's long term average value is 79.23. It's latest available value, on 6/30/2025, is 45.40 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/1997, to it's latest available value, on 6/30/2025, is +186.88%.The Serie's change in percent from it's maximum value, on 6/30/2025, to it's latest available value, on 6/30/2025, is 0.0%.

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

Refer to the 'Current Geographic Boundaries Table' layer for a list of all current geographies and recent updates.

This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 929 SA3s, including 4 non-digitised SA3s.

The SA3 geography aims to meet three purposes:

1. approximate suburbs in major, large, and medium urban areas,
2. in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities,
3. minimise data suppression.

SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations.

Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s.

Zero or nominal population SA3s

To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations.

Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority.

Small population island SA2s are included in their adjacent land-based SA3.

Island SA2s outside territorial authority or region are the same in the SA3 geography.

Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification.

Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority.

Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council.

The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name):

70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region.

SA3 numbering and naming

Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb, recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District).

SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2025, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained.

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High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007

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Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

Dataset for Beginners to start Data Science process. The subject of data is about simple clinical data for problem definition and solving. range of data science tasks such as classification, clustering, EDA and statistical analysis are using with dataset.

columns in data set are present: Age: Numerical (Age of patient) Sex: Binary (Gender of patient) BP: Nominal (Blood Pressure of patient with values: Low, Normal and High) Cholesterol: Nominal (Cholesterol of patient with values: Normal and High) Na: Numerical (Sodium level of patient experiment) K: Numerical (Potassium level of patient experiment) Drug: Nominal (Type of Drug that prescribed with doctor, with values: A, B, C, X and Y)

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Luxembourg. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 211.88 as of 6/30/2025, the highest value since 9/30/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.56 percent compared to the value the year prior.The 1 year change in percent is 4.56.The 3 year change in percent is -9.87.The 5 year change in percent is 14.19.The 10 year change in percent is 70.02.The Serie's long term average value is 145.80. It's latest available value, on 6/30/2025, is 45.32 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/2007, to it's latest available value, on 6/30/2025, is +133.67%.The Serie's change in percent from it's maximum value, on 9/30/2022, to it's latest available value, on 6/30/2025, is -11.70%.

Time series data for the statistic Gross Domestic Product, Nominal, Unadjusted, Domestic Currency and country Cabo Verde. Indicator Definition:Gross Domestic Product, Nominal, Unadjusted, Domestic CurrencyThe indicator "Gross Domestic Product, Nominal, Unadjusted, Domestic Currency" stands at 74.03 Billion as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.10 percent compared to the value the year prior.The 1 year change in percent is 7.10.The 3 year change in percent is 37.01.The 5 year change in percent is 39.51.The 10 year change in percent is 72.90.The Serie's long term average value is 47.09 Billion. It's latest available value, on 3/31/2025, is 57.19 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/2007, to it's latest available value, on 3/31/2025, is +132.75%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 3/31/2025, is -0.513%.

Time series data for the statistic Gross Domestic Product, Nominal, Unadjusted, Domestic Currency and country Turkey. Indicator Definition:Gross Domestic Product, Nominal, Unadjusted, Domestic CurrencyThe indicator "Gross Domestic Product, Nominal, Unadjusted, Domestic Currency" stands at 12.13 Trillion as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 36.70 percent compared to the value the year prior.The 1 year change in percent is 36.70.The 3 year change in percent is 381.20.The 5 year change in percent is 1,033.07.The 10 year change in percent is 2,322.74.The Serie's long term average value is 1.28 Trillion. It's latest available value, on 3/31/2025, is 843.97 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/1998, to it's latest available value, on 3/31/2025, is +90,055.85%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 3/31/2025, is -4.56%.

Time series data for the statistic Gross Domestic Product, Nominal, Unadjusted, Domestic Currency and country Indonesia. Indicator Definition:Gross Domestic Product, Nominal, Unadjusted, Domestic CurrencyThe indicator "Gross Domestic Product, Nominal, Unadjusted, Domestic Currency" stands at 5.95 Quadrillion as of 6/30/2025, the highest value at least since 6/30/2008, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 7.41 percent compared to the value the year prior.The 1 year change in percent is 7.41.The 3 year change in percent is 21.42.The 5 year change in percent is 61.13.The 10 year change in percent is 107.36.The Serie's long term average value is 3.28 Quadrillion. It's latest available value, on 6/30/2025, is 81.24 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/2008, to it's latest available value, on 6/30/2025, is +392.59%.The Serie's change in percent from it's maximum value, on 6/30/2025, to it's latest available value, on 6/30/2025, is 0.0%.