Please note, this dataset has been superseded by a newer version (see below).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.

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%.

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%.

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.

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

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%.

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]

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

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%.

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:

4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.

3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.

10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.

5 million timestamps. Sensors readings are at 1Hz sampling frequency.

1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.

4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).

200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.

Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.

Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.

Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.

Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).

Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.

Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.

Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.

No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

Change Log

Version 2

Metadata: we include a metadata.csv with information about:

Anomaly categories

Root cause channel (signal in which the anomaly is first visible)

Affected channel (signal in which the anomaly might propagate) through coupled system dynamics

Removal of anomaly overlaps: version 1 contained anomalies which overlapped with each other resulting in only 190 distinct anomalous segments. Now, there are no more anomaly overlaps.

Two data files: CSV and parquet for convenience.

[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

About Solenix

Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.

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 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%.

Statistical area 3 (SA3) is a new output geography, introduced in 2023, that allows aggregations of population data between the SA2 geography and territorial authority geography.

​

This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2023 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 2023, 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.

​

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

​

Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

​

Macrons

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

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

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

About Dataset

There has been a revenue decline in the Portuguese Bank and they would like to know what actions to take. After investigation, they found that the root cause was that their customers are not investing enough for long term deposits. So the bank would like to identify existing customers that have a higher chance to subscribe for a long term deposit and focus marketing efforts on such customers.

Data Set Information

The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed.

There are two datasets: train.csv with all examples (32950) and 21 inputs including the target feature, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]

test.csv which is the test data that consists of 8238 observations and 20 features without the target feature

Goal:- The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

The dataset contains train and test data. Features of train data are listed below. And the test data have already been preprocessed.

Features

Target variable (desired output):

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/

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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%.

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).

Time series data for the statistic Gross Domestic Product, Nominal, Unadjusted, Domestic Currency and country Slovak Republic. Indicator Definition:Gross Domestic Product, Nominal, Unadjusted, Domestic CurrencyThe indicator "Gross Domestic Product, Nominal, Unadjusted, Domestic Currency" stands at 34.44 Billion as of 6/30/2025, the highest value at least since 6/30/1995, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.10 percent compared to the value the year prior.The 1 year change in percent is 5.10.The 3 year change in percent is 25.86.The 5 year change in percent is 56.14.The 10 year change in percent is 72.14.The Serie's long term average value is 16.71 Billion. It's latest available value, on 6/30/2025, is 106.08 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/1995, to it's latest available value, on 6/30/2025, is +645.52%.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%.

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country Iceland. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 366.71 as of 06/30/2025, the highest value at least since 06/30/1981, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.85 percent compared to the value the year prior.The 1 year change in percent is 5.85.The 3 year change in percent is 21.36.The 5 year change in percent is 67.96.The 10 year change in percent is 165.53.The Serie's long term average value is 96.88. It's latest available value, on 06/30/2025, is 278.50 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 03/31/1981, to it's latest available value, on 06/30/2025, is +16,550.29%.The Serie's change in percent from it's maximum value, on 06/30/2025, to it's latest available value, on 06/30/2025, is 0.0%.

Time series data for the statistic Nominal Residential Property Price Index Quarterly and country North Macedonia. Indicator Definition:Nominal Residential Property Price Index QuarterlyThe indicator "Nominal Residential Property Price Index Quarterly" stands at 184.14 as of 6/30/2025, the highest value at least since 6/30/2000, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 22.34 percent compared to the value the year prior.The 1 year change in percent is 22.34.The 3 year change in percent is 49.67.The 5 year change in percent is 84.60.The 10 year change in percent is 100.50.The Serie's long term average value is 95.62. It's latest available value, on 6/30/2025, is 92.57 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 3/31/2000, to it's latest available value, on 6/30/2025, is +223.68%.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%.