Index figures on production prices of dwellings and other buildings reflect the relation between the output value and the output volume and can be used to convert the value of construction output from current prices to fixed prices. The output price index is derived from the series "New dwellings; output indices 2000=100". From the 2nd quarter 2009 on, the figures of the series 2005 = 100 are used and linked to the series 2000 = 100. Statistics Netherlands publishes data on the value of construction output. The volume of construction output, however, cannot be deduced from the value, which is subject to price changes. The price index on the building costs of new dwellings eliminates the effect of price changes. The price index on construction output is calculated by distributing the value of the output (current prices) over the quarters essential to the price setting of the building project. Subsequently, the quarterly output is calculated in fixed prices by using the price index on the building costs of new dwellings. The index figure of the output price is the sum of the current prices divided by the sum of the fixed prices (*100).

Possibilities for selection: - Total construction - Total construction of new dwellings/buildings - New dwellings - New buildings in the private sector - New buildings in the non-commercial sector - Total other buildings - Other dwellings - Other buildings in the private sector - Other buildings in the non-commercial sector

Data available from 1st quarter 2000 till 4th quarter 2016 Frequency: discontinued

Status of the figures: The figures of 2016 are provisional. Since this table has been discontinued, the data will not become definitive.

Changes as of January 29 2018 None, this table is discontinued.

When will new figures become available? This table is succeeded by Production on buildings; price index 2015 = 100. See paragraph 3.

Linking recommendation If you want to compile long-term series with linked price indices on production of buildings, you can link the figures on price level 1995 with the figures on price level 2000. For that, the percentage change from the 2nd quarter 2005 with the 1st quarter 2005 must be calculated, as the price index for the 1st quarter 2005 is the last figure published on price level 1995. This change must then be adjusted to the figures for the 1st quarter 2005 of the series 1995. The 2nd quarter index of the linked series is calculated by calculating the difference between the 1st quarter 2005 and the 2nd quarter 2005 according to the series on price level 2000 and multiplying this by the index for the 1st quarter 2005 according to the series on price level 1995.

In the example: (119/120) x 148=147 (rounded). For the 3rd quarter 2005 the index is calculated analogously, where because of rounding problems the first quarter figures must be used for the link.

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

This table shows the price indices, quarterly and yearly changes in prices of services that companies provide. The figures are broken down by type of services according to the Classification of Products by Activity (CPA 2015 version 2.1). For some services, a further breakdown has been made on the basis of market data that differ from the CPA. This breakdown is indicated with a letter after the CPA-code.

The base year for all Services producer price indices is 2021. The year average, quarterly and yearly changes are calculated with unrounded figures.

Data available from: 4th quarter 2002.

Status of the figures: The figures for the most recent quarter are provisional. These figures are made definite in the publication for the subsequent quarter.

Changes as of November 14 2025: The provisional figures of the 3rd quarter 2025 are published for approximately half of the branches. All previous figures are made definite. For all other branches the figures of the 3rd quarter 2025 are available at a later date.

When will new figures be published? New figures are available twice per quarter. Halfway each quarter, the results of the pricing method Model pricing (around half of the branches) are published and the other branches with the Unit value method follow at the end of the quarter. This concerns the price development of the previous quarter. The Services producer price index of the total commercial services is also calculated and published at the end of each quarter.

The Services producer price indices publication schedule can be downloaded as an Excel file under section: 3 Relevant articles. More information about the pricing method can be found in the video under section: 3 Relevant articles.

Description

Dataset consists of data in categories walking, running, biking, skiing, and roller skiing (5). Sport activities have been recorded by an individual active (non-competitive) athlete. Data is pre-processed, standardized and splitted in four parts (each dimension in its own file): * HR-DATA_std_1140x69 (heart rate signals) * SPD-DATA_std_1140x69 (speed signals) * ALT-DATA_std_1140x69 (altitude signals) * META-DATA_1140x4 (labels and details)

NOTE: Signal order between the separate files must not be confused when processing the data. Signal order is critical; first index in each of the file comes from the same activity which label corresponds to first index in the target data file, and so on. So, data should be constructed and files combined into the same table while reading the files, ideally using nested data structure. Something like in the picture below:

You may check the related TSC projects in GitHub: - "https://github.com/JABE22/MasterProject">Sport Activity Classification Using Classical Machine Learning and Time Series Methods - Symbolic Representation of Multivariate Time Series Signals in Sport Activity Classification - Kaggle Project

https://mediauploads.data.world/e1ccd4d36522e04c0061d12d05a87407bec80716f6fe7301991eaaccd577baa8_mts_data.png" alt="Nested data structure for multivariate time series classifiers">

In the following picture one can see five signal samples for each dimension (Heart Rate, Speed, Altitude) in standard feature value format. So, each figure contains signal from five different random activities (can be same or different category). However, for example, signal indexes number 1 in each three figure are from the same activity. Figures just visualizes what kind of signals dataset consists. They do not have any particular meaning.

https://mediauploads.data.world/162b7086448d8dbd202d282014bcf12bd95bd3174b41c770aa1044bab22ad655_signal_samples.png" alt="Signals from sport activities (Heart Rate, Speed, and Altitude)">

Dataset size and construction procedure

The original amount of sport activities is 228. From each of them, starting from the index 100 (seconds), have been picked 5 x 69 second consecutive segments, that is expressed as a formula below:

https://mediauploads.data.world/68ce83092ec65f6fbaee90e5de6e12df40498e08fa6725c111f1205835c1a842_segment_equation.png" alt="Data segmentation and augmentation formula">

where 𝐷 = 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑓𝑖𝑙𝑡𝑒𝑟𝑒𝑑 𝑑𝑎𝑡𝑎 ,𝑁 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 , 𝑠 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑠𝑡𝑎𝑟𝑡 𝑖𝑛𝑑𝑒𝑥 , 𝑙 = 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡ℎ, and 𝑛 = 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑒𝑔𝑚𝑒𝑛𝑡𝑠 from a single original sequence 𝐷𝑖 , resulting the new set of equal length segments 𝐷𝑠𝑒𝑔. And in this certain case the equation takes the form of:

https://mediauploads.data.world/63dd87bf3d0010923ad05a8286224526e241b17bbbce790133030d8e73f3d3a7_data_segmentation_formula.png" alt="Data segmentation and augmentation formula with values">

Thus, dataset has dimesions of 1140 x 69 x 3.

Additional information

Data has been recorded without knowing it will be used in research, therefore it represents well real-world application of data source and can provide excellent tool to test algorithms in real data.

Recording devices

Data has been recorded using two type of Garmin devices. Models are Forerunner 920XT and vivosport. Vivosport is activity tracker and measures heart rate from the wrist using optical sensor, whereas 920XT requires external sensor belt (hear rate + inertial) installed under chest when doing exercises. Otherwise devices are not essentially different, they uses GPS location to measure speed and inertial barometer to measure elevation changes.

Device manuals - Garmin FR-920XT - Garmin Vivosport

Person profile

Age: 30-31, Weight: 82, Length: 181, Active athlete (non-competitive)

ABSTRACT The objective of this study was to develop a feedlot beef cattle calculation model and production cost analysis and, from the results obtained, devise a production cost index. A case study was conducted to understand the characteristics of the productive processes of a commercial feedlot. Then, based on the Economic Theory, cost items of the farm under analysis were identified and transferred to a spreadsheet. The survey included ten feedlot farmers from the state of São Paulo and other nine from the state of Goiás and was carried out to determine representative properties, and prices of items used were monitored. Production costs of each farm were calculated, and theoretical concepts of index numbers were used to devise the feedlot cattle production cost index. The cost allocation scheme was divided into four cost groups: variable, semi-fixed, fixed, and production remuneration factors. The developed model allowed a cost prognosis of the analyzed systems. Highest total costs for São Paulo State feedlots were R$ 9.17 kg−1 and R$ 9.08 kg−1 for average-sized and large farms, respectively, as contrasted to that of Goiás, of R$ 8.29 kg−1. Between the months April and June, the cost of production for feedlot beef cattle showed reductions of 1.48 and 1.40% for the average and large feedlots in the State of São Paulo and 9.13% for the Goiás feedlot by the Konüs Exact Index, respectively. Studies available in literature were compared and it was concluded that the model can help feedlot cattle farmers take production decisions. The Konüs Index allows for a methodological advancement in relation to other studies carried out on the Brazilian livestock industry; besides, it can contribute to the sector organization.

This table provides information on price developments in the construction industry. Data were calculated by Statistic Netherlands (CBS) and are based on building permits with a value of 50 thousand euros or more issued by municipal authorities, and the reported construction costs as stated in the permits. On the basis of these building permits and the construction time, construction output is calculated by means of average waiting times prior to the start of the construction activities. Price indices listed in the table are used to eliminate the effect of price changes on the construction output. Therefore, the price index can be used to as a deflator to calculate volume developments in the building sector. Price indices are calculated for two sections (Construction of new buildings and Other buildings) and three sectors (dwellings, buildings for the private sector and buildings for the (semi-)public or non-commercial sector).

Data available from: 1st quarter 2015

Status of the figures: Price index figures up to and including the 3rd quarter 2024 are final.

Changes since 30 October 2025: The figures of the 3rd quarter 2025 have been added to the table.

When will new figures become available? Provisional figures for the 4th quarter of 2025 will be released in January 2026.

This table includes figures on year-on-year developments of expenditure categories of the Harmonised consumer price index (HICP). This table also contains the weighting coefficient. The weighting coefficient shows how many consumers in the Netherlands spend on a product group in relation to their total expenditure.

Furthermore, the table shows the contribution and impact of HICP categories. The contributions of the separate groups add up to the total annual rate of change and show the share of price increases. The impact, on the other hand, answers the question how much higher or lower the annual rate of change would have been, if a specific category would not have been taken into account in calculation. These figures are shown for 139 product groups. Furthermore, 34 combinations of product groups (special aggregates) are displayed.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 2016.

Status of the figures: The HICP figures in this table are in most cases final immediately upon publication. The figures of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis.

In most cases, all requested price information is known to Statistics Netherlands when the results are published and no adjustment is made later. However, sometimes certain price information is not available in time and the outcome can be adjusted later. HICP results can then always be revised together with the CPI results, even if they were not published as provisional in the previous month. CPI results are marked as provisional when the index figures are first published, the figures are final the following month.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 9 June 2022: The unit of the contribution to annual rate of change and the impact on the annual rate of change has been adjusted to 'percentage point'. Previously, the unit was incorrectly referred to as 'percent' in the table.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

Work Function and Cleavage Energy Database

This database contains the work function and cleavage energy calculation results (calculated by high-throughput density functional theory).

Databases

The content of the two database files is described here. The first one contains results from the work function and cleavage energy calculations of the whole generated surface database for unrelaxed slabs. The second one contains the selected slabs that have work functions below 2 or above 7 eV, which then underwent ionic relaxation.

The first database JSON file WF-CE_database_58332.json contains 58,332 records each containing the following information:

mpid: (string) Materials Project ID

miller_index: (list) Miller index of surface hkl as a list of length 3

term: (int) Termination number indexing (unique terminations are numbered starting at 1)

surface_elements: (list) List of atomic numbers present in the topmost atomic layer

surface_elements_string: (str) Same information as surface_elements but as a concatenated string of elements

WF: (float) The DFT-calculated work function in eV of unrelaxed slab of the top surface

cleavage_energy: (float) The DFT-calculated cleavage energy in eV/A^2 of unrelaxed slab

slab: (str) The unrelaxed surface slab as a Pymatgen dictionary represented as a string (use eval to recover the dictionary which can be read by pymatgen)

energy: (float) Total energy of unrelaxed slab in eV

bulk_energy: (float) Total energy of the bulk (reoriented in the direction of the Miller index) in eV

Fermi: (float) Fermi level in eV -

convergence: (list) DFT convergence parameters, a list containing E_cutoff, k_x, k_y, k_z

nsites: (int) Number of atomic sites in the slab unit cell

nterm: (int) Number of unique terminations for given orientation/material

thickness_n: (int) Slab thickness in numbers of bulk unit cells

thickness_A: (float) Slab thickness in Angstroms

area: (float) Surface area in A^2

sym: (bool) Whether or not there exists a mirror or glide plane parallel to the surface, or a 2-fold rotation axis normal to the surface (without adding vacuum). This is equivalent to the surface being non-polar or polar.

sym_vac: (bool) Same as sym but after adding vacuum in the c-direction.

The second database JSON file WF-CE_database_relaxed_90.json contains 90 records each containing the information above (except WF, slab, cleavage_energy, and energy have been renamed to WF_unrelaxed, slab_unrelaxed, cleavage_energy_unrelaxed, and energy_unrelaxed, respectively) and in addition, the database has the following additional fields:

WF_relaxed: (float) The DFT-calculated work function in eV of relaxed slab of the top surface

cleavage_energy_relaxed: (float) The DFT-calculated cleavage energy in eV/A^2 of relaxed slab

slab_relaxed: (str) The relaxed surface slab as a Pymatgen dictionary represented as a string (use eval to recover the dictionary which can be read by pymatgen)

energy_relaxed: (float) Total energy of relaxed slab in eV

icsd: (list) List of ICSD numbers of the bulk crystal

formula: (str) Chemical formula of the bulk

spacegroup: (int) The spacegroup number

How to use

The databases can be loaded with pandas as follows:import pandas as pddata = pd.read_json('./WF-CE_database_58332.json')print(data['WF'].mean())

Gridded datasets used in Jones et al. (2021) paper 'Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems'. Details of how datasets were made and underlying sources are provided in Jones et al. (2021) Supplementary Information. Datasets included: - H_2010_spam_V2r0_42c: crop commodity diversity (Shannon's diversity index) at 10x10km resolution, based on SPAM 2010 V2 physical area maps - sr_2010_spam_v2r0_42c: crop commodity richness at 10x10km resolution, based on SPAM 2010 V2 physical area maps - sr_2010_spam_v2r0_42c_maj22: locations of cropland with at least 22 crop commodities (1) versus cropland with <22 crop commodities at 10x10km resolution, based on SPAM 2010 V2 physical area maps - Livestock_8_shannons_LSU: livestock diversity (Shannon's diversity index) calculated from population numbers converted to standard livestock units at 1x1km resolution, based on Global Livestock of the World v3 - Fish_srichness raster: freshwater fish species richness per major river basin, based on Tedesco et al (2017) - CropPasture_2000_bool: locations where cropland and pasture co-exist (1) versus locations where either cropland OR pasture exist (0), at 10x10km resolution, based on cropland and pasture maps for the year 2000 available from EarthStat - esa2015_natag_1km_pc: percentage of natural or semi-natural vegetation within a 1x1km window around cropped pixels, based on European Space Agency Climate Change Initiative (ESA-CCI) land cover maps for 2015 Not uploaded (no post-processing so data can be accessed at source): - potential soil biodiversity index (see https://esdac.jrc.ec.europa.eu/content/global-soil-biodiversity-atlas) - tree cover on agricultural land (see Zomer et al. 2016 and https://apps.worldagroforestry.org/global-tree-cover/index.html)

The Regional Price Index contrasts the cost of a common basket of goods and services at a number of regional locations to the Perth metropolitan area. The RPIs were commissioned to assist with the calculation of the Western Australian State Government’s regional district allowance, and it has been used to assist in policy decision-making. Show full description

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive).

Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.

All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').

Version 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.

Additionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset.

Data collection and processing Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries. Time and place The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries.
Instruments The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamandé et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006). Resources Web of Science, Scopus – literature search Endnote Web – removal of duplicates Rayyan software – initial paper selection based on title and abstract WebPlotDigitizer – data extraction from figures Microsoft Access – database platform Description of the collected data (column, unit, and description) Sample ID- A unique identification number assigned to each individual sample within the database
Study ID- Identification number assigned to each research study in the database Reference - Research paper reference Year - Year of research paper publication
Language - Language of the research paper
Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers Soil classification (original in paper) - Soil classification described in research paper Soil classification (convertion to Soil Taxonomy orders) - Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA)
Location - Study location country
Texture classification (USDA) - Soil textural classification according USDA Texture classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) Sand (USDA) - % - Soil sand content (weight based) - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available Clay - g kg-1 - Soil clay content - original in the paper Clay class upper boundary - µm - The clay class upper boundary informed in source publication Silt - g kg-1 - Silt clay content - original in the paper Silt class upper boundary - µm - The silt class upper boundary informed in source publication Sand - Soil sand content - original in the paper Sand class upper boundary - µm - The sand class upper boundary informed in source publication Particle size data flag - =0 if no issues; =1 if there are issues (summing) Sum particle size- g kg-1 - Sum of clay, silt, and sand content Soil depth FROM – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected Soil depth TO – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected Depth – cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58 SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations Particle density - Mg m-3 - Soil particle density Initial matric potential – hPa - Soil water matric potential before loading log Initial matric potential - Soil water matric potential expressed by log Wetness (based on initial matric potential) - 1=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported Initial water content data source - Graph or table from where the data was collected, or explanation on calculation used Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples
Initial bulk density - Mg m-3 - Soil bulk density before loading
Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used
Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density Precompression stress – kPa - Precompression stress
Precompression stress (SD) – kPa - Standard deviation for precompression stress values reported in paper
Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used Compression index - Compression index
Compression index (SD) - Standard deviation of compression index values reported in paper
Compression index data source - Graph or table from where the data was collected, or explanation on calculation used Swelling index - Swelling index
Swelling index (SD) - Standard deviation of swelling index values reported in paper
Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used N - Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported Land use (paper) - Land use described in the research paper Land use (categories) - Land use categorized Land use standardized - Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna Land use (number code) - Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation Tillage system - Tillage system Tillage system (arable soils) - Tillage system for arable soils classified as "conventional" and "conservation" Coordinates - Geographical coordinates of study location Climate - Climatic region classification: temperate, tropical, subtropical Climatecod - Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical Sampling position (paper) - Field position where soil samples were collected with details described in the paper Sampling position - Field position

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

The index relates to costs ruling on the first day of each month. NATIONAL HOUSE CONSTRUCTION COST INDEX; Up until October 2006 it was known as the National House Building Index Oct 2000 data; The index since October, 2000, includes the first phase of an agreement following a review of rates of pay and grading structures for the Construction Industry and the first phase increase under the PPF. April, May and June 2001; Figures revised in July 2001due to 2% PPF Revised Terms. March 2002; The drop in the March 2002 figure is due to a decrease in the rate of PRSI from 12% to 10¾% with effect from 1 March 2002. The index from April 2002 excludes the one-off lump sum payment equal to 1% of basic pay on 1 April 2002 under the PPF. April, May, June 2003; Figures revised in August'03 due to the backdated increase of 3% from 1April 2003 under the National Partnership Agreement 'Sustaining Progress'. The increases in April and October 2006 index are due to Social Partnership Agreement "Towards 2016". March 2011; The drop in the March 2011 figure is due to a 7.5% decrease in labour costs. Methodology in producing the Index Prior to October 2006: The index relates solely to labour and material costs which should normally not exceed 65% of the total price of a house. It does not include items such as overheads, profit, interest charges, land development etc. The House Building Cost Index monitors labour costs in the construction industry and the cost of building materials. It does not include items such as overheads, profit, interest charges or land development. The labour costs include insurance cover and the building material costs include V.A.T. Coverage: The type of construction covered is a typical 3 bed-roomed, 2 level local authority house and the index is applied on a national basis. Data Collection: The labour costs are based on agreed labour rates, allowances etc. The building material prices are collected at the beginning of each month from the same suppliers for the same representative basket. Calculation: Labour and material costs for the construction of a typical 3 bed-roomed house are weighted together to produce the index. Post October 2006: The name change from the House Building Cost Index to the House Construction Cost Index was introduced in October 2006 when the method of assessing the materials sub-index was changed from pricing a basket of materials (representative of a typical 2 storey 3 bedroomed local authority house) to the CSO Table 3 Wholesale Price Index. The new Index does maintains continuity with the old HBCI. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Oct 2008 data; Decrease due to a fall in the Oct Wholesale Price Index. .hidden { display: none }

This dataset contains all data and code necessary to reproduce the analysis presented in the manuscript: Winzeler, H.E., Owens, P.R., Read Q.D.., Libohova, Z., Ashworth, A., Sauer, T. 2022. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land 11:2018. DOI: 10.3390/land11112018. There are several steps to this analysis. The relevant scripts for each are listed below. The first step is to use the raw digital elevation data (DEM) to produce different versions of the topographic wetness index (TWI) for the study region (Calculating TWI). Then, these TWI output files are processed, along with soil moisture (volumetric water content or VWC) time series data from a number of sensors located within the study region, to create analysis-ready data objects (Processing TWI and VWC). Next, models are fit relating TWI to soil moisture (Model fitting) and results are plotted (Visualizing main results). A number of additional analyses were also done (Additional analyses). Input data The DEM of the study region is archived in this dataset as SourceDem.zip. This contains the DEM of the study region (DEM1.sgrd) and associated auxiliary files all called DEM1.* with different extensions. In addition, the DEM is provided as a .tif file called USGS_one_meter_x39y400_AR_R6_WashingtonCO_2015.tif. The remaining data and code files are archived in the repository created with a GitHub release on 2022-10-11, twi-moisture-0.1.zip. The data are found in a subfolder called data.

2017_LoggerData_HEW.csv through 2021_HEW.csv: Soil moisture (VWC) logger data for each year 2017-2021 (5 files total). 2882174.csv: weather data from a nearby station. DryPeriods2017-2021.csv: starting and ending days for dry periods 2017-2021. LoggerLocations.csv: Geographic locations and metadata for each VWC logger. Logger_Locations_TWI_2017-2021.xlsx: 546 topographic wetness indexes calculated at each VWC logger location. note: This is intermediate input created in the first step of the pipeline.

Code pipeline To reproduce the analysis in the manuscript run these scripts in the following order. The scripts are all found in the root directory of the repository. See the manuscript for more details on the methods. Calculating TWI

TerrainAnalysis.R: Taking the DEM file as input, calculates 546 different topgraphic wetness indexes using a variety of different algorithms. Each algorithm is run multiple times with different input parameters, as described in more detail in the manuscript. After performing this step, it is necessary to use the SAGA-GIS GUI to extract the TWI values for each of the sensor locations. The output generated in this way is included in this repository as Logger_Locations_TWI_2017-2021.xlsx. Therefore it is not necessary to rerun this step of the analysis but the code is provided for completeness.

Processing TWI and VWC

read_process_data.R: Takes raw TWI and moisture data files and processes them into analysis-ready format, saving the results as CSV. qc_avg_moisture.R: Does additional quality control on the moisture data and averages it across different time periods.

Model fitting Models were fit regressing soil moisture (average VWC for a certain time period) against a TWI index, with and without soil depth as a covariate. In each case, for both the model without depth and the model with depth, prediction performance was calculated with and without spatially-blocked cross-validation. Where cross validation wasn't used, we simply used the predictions from the model fit to all the data.

fit_combos.R: Models were fit to each combination of soil moisture averaged over 57 months (all months from April 2017-December 2021) and 546 TWI indexes. In addition models were fit to soil moisture averaged over years, and to the grand mean across the full study period. fit_dryperiods.R: Models were fit to soil moisture averaged over previously identified dry periods within the study period (each 1 or 2 weeks in length), again for each of the 546 indexes. fit_summer.R: Models were fit to the soil moisture average for the months of June-September for each of the five years, again for each of the 546 indexes.

Visualizing main results Preliminary visualization of results was done in a series of RMarkdown notebooks. All the notebooks follow the same general format, plotting model performance (observed-predicted correlation) across different combinations of time period and characteristics of the TWI indexes being compared. The indexes are grouped by SWI versus TWI, DEM filter used, flow algorithm, and any other parameters that varied. The notebooks show the model performance metrics with and without the soil depth covariate, and with and without spatially-blocked cross-validation. Crossing those two factors, there are four values for model performance for each combination of time period and TWI index presented.

performance_plots_bymonth.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by month across the five years of data to show within-year trends. performance_plots_byyear.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by year to show trends across multiple years. performance_plots_dry_periods.Rmd: Prediction performance was presented for the models fit to the previously identified dry periods. performance_plots_summer.Rmd: Prediction performance was presented for the models fit to the June-September moisture averages.

Additional analyses Some additional analyses were done that may not be published in the final manuscript but which are included here for completeness.

2019dryperiod.Rmd: analysis, done separately for each day, of a specific dry period in 2019. alldryperiodsbyday.Rmd: analysis, done separately for each day, of the same dry periods discussed above. best_indices.R: after fitting models, this script was used to quickly identify some of the best-performing indexes for closer scrutiny. wateryearfigs.R: exploratory figures showing median and quantile interval of VWC for sensors in low and high TWI locations for each water year. Resources in this dataset:Resource Title: Digital elevation model of study region. File Name: SourceDEM.zipResource Description: .zip archive containing digital elevation model files for the study region. See dataset description for more details.Resource Title: twi-moisture-0.1: Archived git repository containing all other necessary data and code . File Name: twi-moisture-0.1.zipResource Description: .zip archive containing all data and code, other than the digital elevation model archived as a separate file. This file was generated by a GitHub release made on 2022-10-11 of the git repository hosted at https://github.com/qdread/twi-moisture (private repository). See dataset description and README file contained within this archive for more details.

The historical series 'New dwellings; input price indices of building costs 2000 = 100, from 1990' shows the development of the costs (wages and materials) involved in building new dwellings in the Netherlands, and has become available by linking series which were calculated separately in the past. An input price index is based on price changes in several cost components involved in realising a product, in this case a new dwelling. Changes in other cost components such as building equipment (tools and machines), general costs, profits and risk are not included in the index. Other cost components, such as energy and transport, are also not taken into account as their influence on the final cost price is relatively modest. Land costs are also not included in the index.

From 1990 to December 1994 only figures of the materials are available. From 1995 onwards this series also includes wage figures. Also from 1995, figures are also available on total building costs by weighted aggregetion of these two series.

Data available from: - Materials: January 1990 - Wages: January 1995 - Total building costs: January 1995

Status of the figures: The price index figures for wages and the total construction costs are final until 2024. The figures for building materials are final until May 2025.

Changes as of November 28th, 2025: Figures of October 2025 have been added. Due to an improvement in the calculation method, some index figures are revised by a maximum of 0.4 index point. The improvement relates to the aggregation of sub series.

Changes as of November 29th, 2024: Since this publication, a switch has been made to a different rounding strategy, whereby the changes are calculated on unrounded index figures and annual figures are calculated from rounded and published figures. With this switch there is more consistency with other statistics on Statline and statistics from Eurostat. As a result, mutations have changed across the entire series.

When will new figures be published? New figures are published about 30 days after the month under review.

This table includes figures on the price developments of a package of goods and services purchased by consumers in the Netherlands. The figures are consistent with European directives also known as the harmonised consumer price index (HICP). In all member states of the European Union (EU), these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

This table also contains the HICP at constant taxes: this price index excludes the effect of changes in the rates of product-related taxes (e.g. VAT and excise duty on alcohol and tobacco).

The table also includes the month-on-month and year-on-year changes of the HICP. The year-on-year change of total consumer expenditure is known as inflation. The figures are shown for 327 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed. The weighting coefficient shows how much consumers in the Netherlands spend on each product group in relation to their total expenditure. The total weighting is 100,000.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: Figures of the flash estimate are published at the end of a reporting month, or shortly thereafter. At the flash estimate, figures are made available for the all items category and for a selection of special aggregates. These figures are calculated on the basis of still incomplete source data. The results of the flash estimate are characterized as provisional.

In most cases, the figures are final in the second publication of the same reporting month. Differences between the provisional and final indices are caused by source material that has become available after the flash estimate. The results of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis. In that case, the figures become final one month later.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, price changes will be published for expenditure categories 053290 Other small electric household appliances and 103000 Post-secondary non-tertiary education. The base period for this new index series is December 2024. This means that the index level of 100 is the price level measured in December 2024.

Changes as of 8 February 2024: Starting in the reporting month of January 2024, a price change will be published for expenditure category 063000 Hospital Services. The base period for this new index series is December 2023. This means that the index level of 100 is the price level measured in December 2023. Previously, between 2000 and 2009, an index was published for the same expenditure category. The base year for that index series was 2005=100. It was discontinued after December 2009. The current series starts again from 100 in December 2023.

When will new figures be published? The figures of the flash estimate are published on the last working day of the month to which the figures relate, or shortly thereafter.

Final figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

The Superdiversity dataset includes the Superdiversity Index (SI) calculated on the diversity of the emotional content expressed in texts of different communities. The emotional valences of words used by a community are extracted from Twitter data produced by that specific community. The Superdiversity dataset includes the SI built on Twitter data and lexicon-based Sentiment Analysis. In addition, the dataset comprises other possible diversity measures calculated from the same data from which the SI is calculated, such as the number of tweets in the community language and the Type-Token Ratio, the number of languages in a community. Version 1.1: Data is computed for nine different nations France, Germany, Ireland, Italy, the Netherlands, Poland, Portugal, Spain and the United Kingdom. Note The SI ranges in [0, 1]: a value of 0 means an emotional content very close between the computed valences and a standard emotional lexicon. a value of 0.5 indicates no correlation between the emotional content of words used by the community on Twitter and the standard emotional content. a value of 1 would correspond to the use of terms with the opposite emotional content compared to the standard. Data is computed at different three geographical scales based on the Classification of Territorial Units for Statistics (NUTS), i.e., NUTS1, NUTS2, and NUTS3, for two different nations, Italy and the United Kingdom.

This database contains spatial information with a 0.05° grid resolution of specific agroclimatic indices for maize, dry beans, soybeans, and coffee regions in Angola. In total, the database comprises 13 agroclimatic indices for each crop, grouped as follows: 1. Dry Conditions Indices: • Number of Dry Days • Number of Dry Spells • Average Length of Dry Spells 2. Wet Conditions Indices: • Number of Wet Days • Number of Wet Spells • Average Length of Wet Spells • Total Precipitation 3. Heatwave Indices: • Number of Hot Days • Number of Heatwaves • Maximum Length of Heatwaves 4. Crop Water Requirement Index: • Potential Evapotranspiration (ETo) 5. Water Balance Index: • Standardized Precipitation and Evapotranspiration Index (SPEI) These indices were calculated using historical climatic data for the period 1981 to 2020, considering the typical growth and development periods of each crop of interest, detailed as follows: • Maize: September - April • Beans: November – March • Soybeans: October – April • Coffee: September – August Additionally, six "El Niño" events (1982-1983, 1987-1988, 1991-1992, 1997-1998, 2009-2010, 2015-2016) and six "La Niña" events (1984-1985, 1988-1989, 1998-1999) were considered to characterize the behavior of each indicator under the influence of different phases of the ENSO phenomenon. Metodology:Regarding the climatic data used to calculate each of the indices, the following information is provided: 1. Dry and Wet Conditions Indices: Historical daily rainfall data from the Climate Hazards Group InfraRed Precipitation Measurement (CHIRPS) dataset (https://www.chc.ucsb.edu/data) were used. 2. Heatwave Indices: Historical daily maximum temperature data were obtained from the AgERA5 database (https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview), and a resampling process was applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. 3. Crop Water Requirement Indices: The Priestley-Taylor equation was used to calculate Potential Evapotranspiration (ETo) due to its simplicity and suitability for tropical conditions. Daily maximum and minimum temperature data, as well as solar radiation, were obtained from the AgERA5 database. A resampling process was also applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. 4. Water Balance Indices: The SPEI indicator calculation was based on daily precipitation data from CHIRPS and ETo calculated using daily maximum and minimum temperature data, as well as solar radiation, from the AgERA5 database. This database provides a valuable tool for understanding and managing agroclimatic aspects in key crop-producing regions in Angola, which can have a significant impact on the country's agriculture and food security.