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.

Dataset

This dataset was created by Michael Nowell

Released under Community Data License Agreement - Sharing - Version 1.0

Contents

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.

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.

Scientific formula for ideal body weight calculation

An equation to properly analyse the MXI proposed by Hoek (2008).

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.;;

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

Indicators are increasingly used to measure environmental systems; however, they are often criticized for failing to measure and describe uncertainty. Uncertainty is particularly difficult to evaluate and communicate in the case of composite indicators which aggregate many indicators of ecosystem condition. One of the ongoing goals of the Ocean Health Index (OHI) has been to improve our approach to dealing with missing data, which is a major source of uncertainty. Here we: (1) quantify the potential influence of gapfilled data on index scores from the 2015 global OHI assessment; (2) develop effective methods of tracking, quantifying, and communicating this information; and (3) provide general guidance for implementing gapfilling procedures for existing and emerging indicators, including regional OHI assessments. For the overall OHI global index score, the percent contribution of gapfilled data was relatively small (18.5%); however, it varied substantially among regions and goals. In general, smaller territorial jurisdictions and the food provision and tourism and recreation goals required the most gapfilling. We found the best approach for managing gapfilled data was to mirror the general framework used to organize, calculate, and communicate the Index data and scores. Quantifying gapfilling provides a measure of the reliability of the scores for different regions and components of an indicator. Importantly, this information highlights the importance of the underlying datasets used to calculate composite indicators and can inform and incentivize future data collection.

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

(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).

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.

The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers (USACE), conducted a study from June 2014 through November 2014 to identify the hydrologic, chemical and microbiologic processes affecting declining pump performance and frequent pump failure at a confined disposal facility (CDF) in East Chicago, Indiana. A decline in groundwater pump performance through time is not uncommon and is generally attributed to biofouling. To better understand the causes behind declining pump performance, data were collected to describe the geochemistry and microbiology of groundwater and solids collected from extraction and monitoring wells at the CDF. Mineral-saturation indices were computed using PHREEQC software (Parkhurst and Appelo, 2013) for groundwater samples collected from extraction wells ( EW-4B, EW-22B, and EW-14A) and monitoring wells (MW-4A, MW-11A, and MW14A) during four sampling regimes between September 9th and November 6th, 2014. In addition, miner ...

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)

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

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).

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.

Topographic Wetness Index (TWI) is calculated as log_e(specific catchment area / slope) and estimates the relative wetness within a catchment.

The TWI product was derived from the partial contributing area product (CA_MFD_PARTIAL), which was computed from the Hydrologically enforced Digital Elevation Model (DEM-H; ANZCW0703014615), and from the percent slope product, which was computed from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016). Both DEM-S and DEM-H are based on the 1 arcsecond resolution SRTM data acquired by NASA in February 2000.

Note that the partial contributing area product does not always represent contributing areas larger than about 25 km2 because it was processed on overlapping tiles, not complete catchments. This only impacts TWI values in river channels and does not affect values on the land around the river channels. Since the index is not intended for use in river channels this limitation has no impact on the utility of TWI for spatial modelling.

The TWI data are available in gridded format at 1 arcsecond and 3 arcsecond resolutions.

The 3 arcsecond resolution TWI product was generated from the 1 arcsecond TWI product and masked by the 3” water and ocean mask datasets. Lineage: Source data 1. 1 arcsecond resolution partial contributing area derived from the DEM-H (ANZCW0703014615). 2. 1 arcsecond resolution slope percent derived from DEM-S (ANZCW0703014016) 3. 3 arcsecond resolution SRTM water body and ocean mask datasets

TWI calculation TWI was calculated from DEM-H following the methods described in Gallant and Wilson (2000). The program uses a slope-weighted multiple flow algorithm for flow accumulation, but uses the flow directions derived from the interpolation (ANUDEM) where they exist. In this case, they are the ANUDEM-derived flow directions only on the enforced stream lines, so the flow accumulation will follow the streams. The different spacing in the E-W and N-S directions due to the geographic projection of the data was accounted for by using the actual spacing in metres of the grid points calculated from the latitude.

Contributing area was converted to specific catchment area using the square root of cell area as the best estimate of cell width on the approximately rectangular cells. The contributing area value was also reduced by half of one grid cell to provide better estimates at tops of hills.

Slope was converted from percent to ratio, as required by the TWI calculation, by dividing by 100. A minimum slope of 0.1% was imposed to prevent division by zero.

The TWI calculation was performed on 1° x 1° tiles, with overlaps to ensure correct values at tile edges.

The 3 arcsecond resolution version was generated from the 1 arcsecond TWI product. This was done by aggregating the 1” data over a 3 x 3 grid cell window and taking the mean of the nine values that contributed to each 3” output grid cell. The 3” TWI data were then masked using the SRTM 3” ocean and water body datasets.

Note that the limitation of partial contributing area due to tiled processing, so that catchment areas extending beyond about 5 km from a tile edge are not captured, has little impact on topographic wetness index. TWI is useful as a measure of position in the landscape on hillslopes (not river channels) and all hillslope areas will be accurately represented by the partial contributing area calculations.

Some typical values for TWI in different positions on the landscape are:

Position Specific catch. Slope (%) TWI area (m)
Upper slope 50 20 5.5 Mid slope 150 10 7.3 Convergent lower 3000 3 11.5 slope

In channels, some typical values would be (using flow width of 30 m):

Contributing Specific catch. Slope (%) TWI area (km2) area (103 m) 1 33 1 15.0 25 833 0.5 18.9 1000 33,333 0.1 24.2

Values of TWI larger than about 12 are most likely in channels or extremely flat areas where the physical concepts behind TWI are invalid and probably are not useful for measuring relative wetness, topographic position or any other geomorphic property. Contributing area (for channels) and MrVBF are more likely to be useful indicators of geomorphic properties in these areas. See, for example, McKenzie, Gallant and Gregory (2003) where soil depth is estimated using TWI on hillslopes and MrVBF in flat valley floors: the range of validity for TWI in that example was approximately 4.8 to somewhat beyond 8.5.

Hence the omission of contributing areas larger than about 25 km2 has no effect on the practical applications of TWI.

Gallant, J.C. and Wilson, J.P. (2000) Primary topographic attributes, chapter 3 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.

McKenzie, N.J., Gallant, J.C. and Gregory, L. (2003) Estimating water storage capacities in soil at catchment scales. Cooperative Research Centre for Catchment Hydrology Technical Report 03/3.

An analytical method used to efficiently calculate a tournament's "relative intransitivity index" (RI).

Scores from the 2016 global Ocean Health Index (OHI) assessment and accompanying data and models. OHI scores were calculated for 220 countries and territories (described in OHI_regions_global.csv) for scenario years 2012 to 2016 (eez2012 to eez2016). Each eez folder contains: (1) scores.csv, which includes, for each region, an overall Index score and scores for the 10 goals (and 8 subgoals) that comprise the Index score. We also provide scores for the status, pressure, resilience, trend, and likely future status (calculated using status, pressure, resilience, and trend) dimensions which are used to calculate Index and goal scores; (2) layers folder, which includes the data used to calculate OHI scores for each region (layers_descriptions.csv provides a brief description, and see http://ohi-science.org/ohi-global/layers.html for complete description); and, (3) conf folder, which includes pressure/resilience matrices and the models (functions.R) used to calculate the status and trend dimension of each goal. The complete ohi-global repository (with all R code) is available from Github (https://github.com/OHI-Science/ohi-global/releases). The data and scripts used to prepare the data are available from Github (https://github.com/OHI-Science/ohiprep/releases). The R package used to calculate scores is also available from Github (https://github.com/OHI-Science/ohicore/releases). Please see our website for additional resources and information (http://ohi-science.org/).