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.;;

The formulation of science and technology financial policies directly influences the direction of national economic development. Quantitative evaluation of these policies is an important method to reflect the consistency and strengths and weaknesses of policy interrelations. This paper analyzes 16 science and technology financial policy documents issued by the Chinese central government from 2016 to 2022, using text analysis and content analysis to extract keyword frequencies, and constructs 9 primary variables and 34 secondary variables. For the first time, a PMC-AE index model for science and technology financial policies is established, and a quantitative evaluation is conducted on 5 significant policy documents out of the 16. The results show that, from an overall analysis, Policy 1 and Policy 4 are at a good level, while the other three policies are at an excellent level. From the analysis of individual policy PMC-AE indexes, the rankings in descending order are: P2 > P5 > P3 > P4 > P1. Overall, the policies effectively meet the needs of China’s science and technology financial development, with P2, P3, and P5 being at an excellent level, P4 at a good level, and P1 at an acceptable level, mainly reflecting the need for improvement in aspects such as policy synchronization with the current stage, targeted entities, guiding fields, and policy content. It is recommended that Chinese government departments should focus on five aspects in policy formulation: building a talent system for science and technology finance, improving the quality of financial services, coordinating central and local financial policies, protecting intellectual property rights in science and technology finance, and strengthening financial supervision. This will be conducive to the effective implementation of science and technology financial policies.

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

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).

CrossDI-DatasetCrossDI(Cross-source Disruption Indexes) Dataset1.OverviewPython tools and curated data to compute disruption-style metrics in yearly windows. Only the citing side is time-truncated. Metrics are produced per target paper and per window, with multi-source support and parallel processing.2.DomainsFile names use an ID 1-4:1.Synthetic Biology2.Astronomy & Astrophysics3.Blockchain-based Information System Management4.Socio-Economic Impacts of Biological Invasions3.Data File Layoutdataset├─ doi/│ ├─ dois-1.csv│ ├─ dois-2.csv│ ├─ dois-3.csv│ └─ dois-4.csv│ └─ dois-multi-1.csv│ └─ dois-multi-2.csv│ └─ dois-multi-3.csv│ └─ dois-multi-4.csv├─ target/│ ├─ target-1.csv│ ├─ target-2.csv│ ├─ target-3.csv│ └─ target-4.csv├─ citations/│ ├─ citations-1-DIMENSIONS.csv│ ├─ citations-1-OPEN_CITATIONS.csv│ ├─ citations-1-WEB_OF_SCIENCE.csv│ ├─ citations-2-DIMENSIONS.csv│ ├─ citations-2-OPEN_CITATIONS.csv│ ├─ citations-2-WEB_OF_SCIENCE.csv│ ├─ citations-3-DIMENSIONS.csv│ ├─ citations-3-OPEN_CITATIONS.csv│ ├─ citations-3-WEB_OF_SCIENCE.csv│ ├─ citations-4-DIMENSIONS.csv│ ├─ citations-4-OPEN_CITATIONS.csv│ └─ citations-4-WEB_OF_SCIENCE.csv└─ result/ ├─ results-1-DIMENSIONS.xlsx ├─ results-1-OPEN_CITATIONS.xlsx ├─ results-1-WEB_OF_SCIENCE.xlsx └─ same pattern for domains 2–44.File DefinitionsArticle list: doi/dois-{ID}.csv (TSV: doi, year). Only citing articles must have a valid year; cited references may have blank year.Citation edges: citations/citations-{ID}-{SOURCE}.csv (TSV: cited_doi, citing_doi). Direction is cited → citing.Target articles: target/target-{ID}.csv (TSV: doi). Focal papers to be evaluated.Results: result/results-{ID}-{SOURCE}.xlsxMulti-DOI consolidation (doi/dois-multi-{ID}.csv): each line lists a group of normalized DOIs determined to refer to the same work; the first DOI is taken as the canonical identifier (subsequent DOIs are aliases).5.WindowingFor a target published in year y, window Y includes citing papers with year ≤ y + Y.6.DI MetricsFor each (Source, Target, Y) the script reports: DI, mDI, DI5, DInoR, DI3%, DEP, invDEP, Origbase, Destabilization (D), Consolidation (C).7.Output columnsN_F, N_B, N_R, DI, mDI, N_B^5, DI_5, DI^noR, N_F_new, N_B_new, DI_3%, DEP, Orig_base, Destabilization(D), Consolidation(C), DOI, Publication year, Y, Source, invDEP8.References[1] Shuo Xu, Congcong Wang, Xin An, and Jianhua Liu, 2025. CrossDI: A Comprehensive Dataset Crossing Three Databases for Calculating Disruption Indexes. Scientific Data. (Under review)[2] Shuo Xu, Congcong Wang, Xin An, Yunkang Deng, and Jianhua Liu, 2025. Do OpenCitations and Dimensions Serve as an Alternative to Web of Science for Calculating Disruption Indexes? Journal of Informetrics, Vol. 19, No. 3, pp. 101685.[3] Shuo Xu, Liyuan Hao, Xin An, Dongsheng Zhai, and Hongshen Pang, 2019. Types of DOI Errors of Cited References in Web of Science with a Cleaning Method. Scientometrics, Vol. 120, No. 3, pp. 1427-1437.

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 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

ABSTRACT A key step for any modeling study is to compare model-produced estimates with observed/reliable data. The original index of agreement (also known as original Willmott index) has been widely used to measure how well model-produced estimates simulate observed data. However, in its original version such index may lead the user to erroneously select a predicting model. Therefore, this study compared the sensibility of the original index of agreement with its two newer versions (modified and refined) and provided an easy-to-use R-code capable of calculating these three indices. First, the sensibility of the indices was evaluated through Monte Carlo Experiments. These controlled simulations considered different sorts of errors (systematic, random and systematic + random) and errors magnitude. By using the R-code, we also carried out a case of study in which the indices are expected to indicate that th empirical Thornthwaite’s model produces poor estimates of daily reference evapotranspiration in respect to the standard method Penman-Monteith (FAO56). Our findings indicate that the original index of agreement may indeed erroneously select a predicting model performing poorly. Our results also indicate that the newer versions of this index overcome such problem, producing more rigorous evaluations. Although the refined Willmott index presents the broadest range of possible values, it does not inform the user if a predicting model overestimate or underestimate the simulated data, resulting in no extra information regarding those already provided by the modified version. None of the indices represents the error as linear functions of its magnitude in respect to the observed process.

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.

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

Excel files using output from the LandSHIFT model to calculate changes in BII in India for the four scenarios and the base year 2010.

We extend our previous work with the Yost Index by adding 90% confidence intervals to the index values. These were calculated using the variance replicate estimates published in association with the American Community Survey of the United States Census Bureau.

In the file yost-tract-2015-2019.csv, the data fields consists of 11-digit geographic ID built from FIPS codes (2 digit state, 3 digit county, 6 digit census tract); Yost index, 90% lower confidence interval; 90% upper confidence interval. Data is provided for 72,793 census tracts for which sufficient data were available. The Yost Index ranges from 1 (lowest socioeconomic position) to 100 (highest socioeconomic position).

For those only interested in using the index as we have calculated it, the file yost-tract-2015-2019 is the only file you need. The other 368 files here are provided for anyone who wishes to replicate our results using the R program yost-conf-intervals.R. The program presumes the user is running Windows machine and that all files reside in a folder called C:/yostindex. The R program requires a number of packages, all of which are specified in lines 10-22 of the program.

Details of this project were published in Boscoe FP, Liu B, LaFantasie J, Niu L, Lee FF. Estimating uncertainty in a socioeconomic index derived from the American Community Survey. SSM-Population Health 2022; 18: 101078. Full text

Additional years of data following this format are planned to be added to this repository in time.