Dataset

This dataset was created by Michael Nowell

Released under Community Data License Agreement - Sharing - Version 1.0

Contents

The Program Access Index (PAI) is one of the measures FNS uses to reward states for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). Performance awards were authorized by the Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill). The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits. The purpose of this step-by-step guide is to describe the calculation of the Program Access Index (PAI) in detail. It includes all of the data, adjustments, and calculations used in determining the PAI for every state.

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.

Scientific formula for ideal body weight calculation

Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources’ impact over time. Our dataset differentiates the context in which citations occur to distinguish between ‘awareness’ and ‘usage’, and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.

Open-file report; contains unpublished data that has not yet been peer-reviewed.

Bangladesh BD: Net Barter Terms of Trade Index data was reported at 68.332 2000=100 in 2020. This records an increase from the previous number of 65.803 2000=100 for 2019. Bangladesh BD: Net Barter Terms of Trade Index data is updated yearly, averaging 103.596 2000=100 from Dec 1980 (Median) to 2020, with 41 observations. The data reached an all-time high of 162.264 2000=100 in 1985 and a record low of 57.575 2000=100 in 2011. Bangladesh BD: Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

This data release provides tabulated liquefaction potential index (LPI) values calculated for a standard set of magnitudes (M), peak ground accelerations (PGA), and groundwater depths (GWD), as described in detail in Engler and others (2025). We use these data to rapidly interpolate LPI values for any M-PGA-GWD combination. The LPI results are computed at cone penetration test (CPT) sites in the San Francisco Bay Area (Holzer and others, 2010). Additionally, the CPT sites are classified using surface geology maps (Wentworth and others, 2023; Wills and others, 2015; Witter and others, 2006).

Water Quality Index Scores for 21 stormwater ponds in Brampton, Ontario. Scores calculated using teh CCME WQI score calculator. Guidelines obtained from CCME resources.

A complete list of live websites using the Bmi Body Mass Index Calculator technology, compiled through global website indexing conducted by WebTechSurvey.

This dataset contains all the intermediate parameters and calculation results of the directional expansion index in the Wuhan Metropolitan Area from 1995 to 2020. Each data is vector data, and the intermediate parameters are in the attribute table of the vector data.

This dataset contains the compilation of the reference concavity analysis calculated for the manuscript "Impact of changing concavity indices on channel steepness and divide migration metrics" - JGR:Earth Surface

Boris Gailleton - boris.gailleton@gfz-potsdam.de
Simon M. Mudd
Fiona J. Clubb
Stuart W.D. Grieve
and Martin D. Hurst

The files are organised by folders, each representing one field site. They contain a csv file with the different information used for table 1 in the main manuscript as well as few useful figures. The summary CSVs have the following collumns:

raster_name: a unique ID
best_fit: the best fit concavity index
err_neg: the lower bound
err_pos: the higher bound
best_fit_norm_by_range: the best fit concavity index (calculated with the range method)
err_neg_norm_by_range: the lower bound (calculated with the range method)
err_pos_norm_by_range: the higher bound (calculated with the range method)
D*_XXX: disorder for each concavity index tested
D*_r_XXX: ranged disorder for each concavity index tested
X_median: the median X coordinate of the basin in local WGS84 - UTM coordinates
X_firstQ: the median X coordinate of the basin in local WGS84 - UTM coordinates
X_thirdtQ: the median X coordinate of the basin in local WGS84 - UTM coordinates
Y_median: the median X coordinate of the basin in local WGS84 - UTM coordinates
Y_firstQ: the median X coordinate of the basin in local WGS84 - UTM coordinates
Y_thirdtQ: the median X coordinate of the basin in local WGS84 - UTM coordinates

The local UTM zones are the following (N: North, S: South):

Andes_Chile: 19S
Arkansas: 15N
Bureinsky_range_russia: 52N
Carpathians: 35N
Caucasus: 38N
Central_sierra_madre: 13N
Corsica: 31N
Ethiopia: 37N
Lesotho: 35S
Luzon_Phillippines: 51S
North_of_Beijing: 50N
Nujang: 46N
Oregon_Coast_Ranges: 10N
San_Gabriel_Mts: 11N
Southern_Altai: 47N
Southern_Brazil: 23S
West_Zoid_Afrika: 33S
Wisconsin: 15N
Yemen: 38N
atlas: 29N
dolomites: 33N
hida: 54N
himalayas: 45N
kentucky_and_west_virginia: 17N
northern_appalachians: 17N
olympic: 10N
pyrenees: 31N
southern_appalachians: 10N
taiwan: 51N
tien_shan: 44N
zagros: 38N

There is also a summary csv file compiling all the information in the root folder.

Most of the field sites also have a number of figures:

_CDF_IQR: Cumulative distributed function of the inter-quartile range of concavity indices' uncertainties for all the basins in the area
_histogram_all_fits: Histogram of all the best fits
_MAP_best_fits: Map of the best fits
_D_star_range_theta_X: Map of D_star_r for the median best fit of all the basins (i.e. how good the median best fit is for each basins)
_min_Dstar_for_each_basins: Map of minimum D_star for each basin, representing the quality of the best fit for each basins

Note that few field sites only have the csv file, as they are themselves compilation of multiple analysis.

All the calculations have been done usign lsdtopytools (10.5281/zenodo.4774992)

The Social Position Index (SPI) makes it possible to apprehend the social status of pupils from the professions and social categories (PCS) of their parents. Each PCS or PCS couple is associated with a numerical value of the IPS. This numerical value is a quantitative summary of a set of socio-economic and cultural attributes related to academic achievement. The higher the Social Position Index (SPI), the more students are on average of favoured social origin. The weaker it is, the more socially disadvantaged the pupils are of origin.

Concretely, the reference values of the index for each PCS, or pair of PCS, are determined using a statistical method that makes it possible to synthesize a set of characteristics describing the living conditions of students (see article by Rocher, 2016). The index of a given CSP is thus the quantitative summary of a number of socio-economic and cultural attributes related to academic success, which are found on average for this CSP.

The first version of the index, used until the start of the 2021 school year, was calculated on the data of the DEPP panel of pupils who entered sixth grade in 2007. For these 35,000 students on the panel, there is rich information on their living conditions to establish the PCS-IPS transition table.

At the start of the 2022 school year, this table of passage was updated by mobilising data from the DEPP panel of pupils who entered CP in 2011.

Once the parents' CSPs are provided, which is the case for the vast majority of second-level students, it is sufficient to apply these reference values and consider this new variable as an index, that is to say, quantitatively. The social level of a school is assessed through the calculation of the average PSI of the students who attend it.

It should be recalled that, like any synthetic index, it is a simplified summary of reality, which cannot by itself account for the complexity of the socio-economic and cultural situation of pupils in an establishment.

As the IPS is based on the PCS declared by families and registered by establishments, it is subject to a certain margin of error: Thus, it is advisable not to over-interpret differences of 3 points or less concerning the average IPS of institutions.

Finally, it should be noted that the methodology for calculating the index has changed, leading to a break in series from the start of the 2022 school year: the reference values of the index have changed and pupils whose GCVs of both parents are not filled in no longer enter into the calculation of the average GPI of their school (transition table available as an attachment below).

In the private sector under contract, changes in the recovery of CSPs took place in September 2023: the second PCS, which had only very partially recovered so far, experienced a very significant change in its availability rate in the bases (from around 15% to 75%). At the same time, it can be observed that the PSIs of private institutions increased at the start of the 2023 school year (+3 points on average), which is directly linked to this development in the second CSP. Thus, developments in IPS among private colleges between 2022 and 2023 need to be interpreted with caution.

The social heterogeneity index of an institution corresponds to the standard deviation of the social position index (SPI) of its pupils. The higher it is, the more diverse the social profile of students. This index has been calculated since the start of the 2019 school year, only for secondary schools. As for the calculation of the average IPS, from the start of the 2022 school year onwards, pupils whose GCVs of both parents are not specified are excluded from the scope of the calculation of the standard deviation.

Field The file provides the average IPS within an institution and the standard deviation of the IPS of its pupils for the French colleges under the supervision of the National Education, public and private under contract, public and private under contract, , calculated from the data of the school year N and for all the pupils of the institution. The file also provides the college’s headcount from which the IPS is calculated. In the file made available, each line corresponds to a college for a given school year.

Reference Rocher, T. (2016). Construction of a social position index for pupils. Education & Training, DEPP, 90, pp.5-27.

Dauphant F., Evain F., Guillerm M., Simon C., Rocher T. (2023), The Social Position Index (SPI): a statistical tool to describe social inequalities between institutions. Information note from the Depp No 23.16.

Find out more about the Social Position Index: https://www.education.gouv.fr/l-index-de-position-sociale-ips-357755

Price index of consumer goods and services is calculated on the basis of the results of: - surveys on prices of consumer goods and services on the retail market, - surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system. Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP). The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

(1) The Human Development Index (HDI) is compiled by the United Nations Development Programme (UNDP) to measure a country's comprehensive development in the areas of health, education, and economy according to the UNDP's calculation formula.(2) Explanation: (1) The HDI value ranges from 0 to 1, with higher values being better. (2) Due to our country's non-membership in the United Nations and its special international situation, the index is calculated by our department according to the UNDP formula using our country's data. The calculation of the comprehensive index for each year is mainly based on the data of various indicators adopted by the UNDP. (3) In order to have the same baseline for international comparison, the comprehensive index and rankings are not retroactively adjusted after being published.(3) Notes: (1) The old indicators included life expectancy at birth, adult literacy rate, gross enrollment ratio, and average annual income per person calculated by purchasing power parity. (2) The indicators were updated to include life expectancy at birth, mean years of schooling, expected years of schooling, and nominal gross national income (GNI) calculated by purchasing power parity. Starting in 2011, the GNI per capita was adjusted from nominal value to real value to exclude the impact of price changes. Additionally, the HDI calculation method has changed from arithmetic mean to geometric mean. (3) The calculation method for indicators in the education domain changed from geometric mean to simple average due to retrospective adjustments in the 2014 Human Development Report for the years 2005, 2008, and 2010-2012. Since 2016, the education domain has adopted data compiled by the Ministry of Education according to definitions from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD).

United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds was 255.57500 Index 1982=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds reached a record high of 303.93800 in September of 2022 and a record low of 83.30000 in March of 1975. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds - last updated from the United States Federal Reserve on December of 2025.

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

• European air quality index (based on pollutant concentrations in µg/m3): hourly measurements for all pollutants for the most recent hour for each station - The sub-indices O3, PM10, PM2.5, NO2, and SO2 are available according to the type of station (industrial, background or traffic) in accordance with the methodology for calculating the European air quality index for the measuring stations throughout the Greater Region. - Data depth: The most recent index for the last 3 hours for each station is available hourly. If the calculation of the index is not possible due to lack of data, its value is marked by a grey dot in the symbology of the map (data not available). In the absence of measurements of certain pollutants (missing sub-indices), the overall index is still calculated on the basis of the pollutants measured at the station concerned. - Data sources: ATMO Grand Est; Agence Wallonne de l'Air et du Climat - AWAC; Landesamt für Umwelt- und Arbeitsschutz Saarland - IMMESA; Landesamt für Umwelt Rheinland-Pfalz - ZIMEN; Administration de l'environnement Luxembourg. Harmonization: ATMO Grand Est and GIS-GR 2020

What is rent indexation? Every year, on the date on which the contract entered into force, the rent for your accommodation may be adjusted to the cost of living. Until December 1993, this indexation was always based on the fluctuations of the consumer price index. From January 1994 onward, the health index became the mandatory basis. Which lease agreements are eligible? Signed Written lease agreement Oral lease agreement Before 28th February 1991 Indexation if both parties provided this Between 28th February 1991 and 31st May 1997 allowed After 31st May 1997 allowed except when excluded in agreement indexation not allowed How is the rent indexation calculated? basic rent (2) X new index (3) The indexed rent = ———————————————— initial index (1) The lease agreement entered into force before 1st January 1984 The lease agreement entered into force on or after 1st January 1984 The lease agreement entered into force on or after 1st January 2019 for a main residence in the Flemish Region agreement signed before 1/01/1981 agreement signed between 1/01/1981 and 31/12/1983 agreement signed before 1/02/1994 agreement signed on or after 1/02/1994 agreement signed on or after 01/01/2019 (1) initial index= December 82 (82,54) (1) initial index= index of the month preceding the adjustment or entry into force of the agreement in 1983 (1) initial index= index of the month proceding the signature signature of the agreement (1) initial index= health index of the month preceding the signature of agreemeent (1) initial index= health index of the month preceding entry into force of the contract or the rent review (2) basic rent = rent: - set by court order - failing such court order, basic rent used in the calculation of the indexed rent in 1990 - failing this, last rent paid in 1983. (2) basic rent = agreed rent (3) new index = the (health) index of the month preceding the anniversary of the entry into force of the agreement Source: FPS Justice, Brussels Housing Code, Walloon Decree on Housing Leases and Flemish Housing Rental Decree Calculate your rent yourself More information Following the 6th state reform, rent indexation has become a regional competence. Since 1st January 2018, 15th March 2018 and 1st January 2019, respectively, new regulations on rent have come into force in the Brussels-Capital Region, in the Walloon Region and in the Flemish Region (Brussels Housing Code, Walloon Decree on Housing Leases and Flemish Housing Rental Decree). If these texts do not regulate a particular aspect of tenancy, the federal legislation on leases is applicable (see the brochure on the law on rents published by the FPS Justice). In concrete terms, only the Flemish Region has modified the indexation calculation. For contracts concluded after the 1st January 2019, the initial index is now the health index of the month preceding the entry into force of the contract, while previously, it was the health index of the month preceding the signature of the lease. For questions on the indexation For questions on legal aspects Statbel (FPS Economy) North Gate - Koning Albert II-laan 16 1000 Brussels Tel. : +32 [0]800 120 33 (9:00 - 17:00) e-mail : ind@economie.fgov.be Flanders https://www.wonenvlaanderen.be/een-woning-huren https://www.woninghuur.vlaanderen/huurdecreet-vanaf-01012019 Wallonia http://lampspw.wallonie.be/dgo4/site_logement/contacts#dept Brussels https://www.belgium.be/en/housing/renting_a_home https://www.baliebrussel.be/nl/kosteloze-rechtshulp/juridische-tweedelijnsbijstand Justitiehuizen Wallonië en Brussel Vlaams Gewest

The Prescott Index is a measure of water balance that has proven to be a useful in soil mapping both to stratify study areas for sampling and as a quantitative predictor of soil properties (Prescott, 1949; McKenzie et al, 2000). The index was designed to give an indication of the intensity of leaching by excess water and is calculated using long-term average precipitation P and potential evaporation E, both expressed as mean monthly values in mm (mean annual values divided by 12):

PI = 0.445P / E^0.75

The evaporation was estimated from temperature and net radiation; the net radiation was computed by the SRAD solar radiation model using the smoothed 1 arc-second resolution DEM-S (ANZCW0703014016) and includes both regional climatic influences and local topographic effects.

Precipitation and temperature were obtained from national climate surfaces averaged over the same time period as the climatic information used in the radiation calculations (1981-2006).

The Prescott Index has no units. Larger values indicate wetter conditions.

The 3 arc-second resolution version of the Prescott Index has been produced from the 1 arc-second resolution surface, by aggregating the cells in a 3x3 window and taking the mean value. Lineage: Source data 1. Mean monthly net radiation calculated by SRAD using the 1 second DEM-S 2. Precipitation at 0.05 degree resolution for the period 1981-2006 (Bureau of Meteorology http://www.bom.gov.au/jsp/awap) 3. Temperature at 0.05 degree resolution, calculated from monthly minimum and maximum air temperature for the period 1981-2006 (Bureau of Meteorology http://www.bom.gov.au/jsp/awap)

Prescott Index calculation Mean annual precipitation for the period 1981-2006 was calculated then divided by 12 to give a single monthly average. A single average monthly temperature was calculated from the mean monthly minimum and maximum temperatures for 1981-2006. Both the precipitation and temperature surfaces were then resampled to 1 arc-second resolution.

A single mean monthly net radiation was calculated from the 12 net radiation surfaces produced by SRAD.

Calculation of Prescott Index requires monthly potential evapotranspiration (mm/month) as an input. The equation used to calculate PET from net radiation is the Priestley-Taylor equation (Priestley and Taylor, 1972) expressed as mm/month: PET = (6.226 + 0.2670T - 0.002130T^2) * RN

Finally, Prescott Index was calculated from mean monthly precipitation and PET: Prescott = (0.445 * precipitation) / (PET ^ 0.75)

The Prescott Index calculation was performed on 1° x 1° tiles at 1 arc-second resolution and the 3 arc-second resolution version was produced by aggregating the 1” cells in a 3x3 window and taking the mean value.