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).

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)

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

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.

Scientific formula for ideal body weight calculation

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.

Dataset

This dataset was created by Michael Nowell

Released under Community Data License Agreement - Sharing - Version 1.0

Contents

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.

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 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.

This file contains the calculations of delta and lambda indices that appear in my article, "The Measurement of Political Cleavages: A New Index, forthcoming in SN Social Sciences

This dataset includes the calculation of the stress index and relative contributions of ph, temperature and salinity on the fertilization rates in the green sea urchin Strongylocentrotus droebachiensis collected in Kongsfjorden in June 2021.

Adults were collected by divers and brought to the installations of the Marine Laboratory in Ny-Ålesund, where they were kept in tanks with water flowing through and were fed with macro algae until the commencement of the experiments (1 week). The adults were induced to spawn by the injection of intracoelomic 0.1M KCl and the gametes were collected separately for quality check. Gametes of two females and four males were selected for the assays. The fertilization assays took place in small 24 multi-well plates and tested four levels of pH (nominal pH 7.1, 7.4, 7.7 and 8.1), four levels of salinity (28, 30, 32 and 34) and three temperatures (1.5°C, 2.2°C and 5.3°C). The experimental design included a factorial combination of the three temperatures and four salinities at ambient pH (pH~8.1), as well as a factorial combination of the same three temperatures and four pH at ambient salinity of 34, resulting in a total of 21 treatments. Each treatment was replicated six times, and the controls were replicated 12 times, adding to a total of 144 experimental units. Each individual well was filled with 2ml of seawater from the corresponding treatment. Subsequently, approximately 200 eggs were added and left to sink to the bottom of the well. After 5 minutes, 1µl of sperm dilution (at a concentration of 3.9 x 106 sperm/ml) was added in each well and the plates were closed and deposited in a water bath at the target temperature for 15 minutes. After that time, one drop of 4% PFA was added to each well to stop the fertilization process and fix the eggs. The appearance of a perivitelline membrane was used as a sign of positive fertilization. The fertilization success in each well was subsequently controlled under a microscope and the number of fertilized vs. unfertilized eggs was noted. The resulting fertilization data has been published independently.

To quantify the relative contribution of each driver to fertilization success, we fitted simple linear regressions for each individual variable (pH, salinity, and temperature), using subsets of the data where the other two variables were held constant at reference levels (pH 8.1, temperature 2.2°C, and salinity 34).

The slope of each regression was used to calculate a comparative index of contribution, standardized relative to a one-unit change in pH (Supplementary 3). Specifically, the contribution of temperature (CT) relative to pH was calculated as the ratio between the slope of the pH model and the slope of the temperature model (slope_pH / slope_temp). Similarly, the contribution of salinity (CS) was derived from the ratio between the slope of the pH model and the slope of the salinity model (slope_pH / slope_sal). This approach allowed us to express each stressor's contribution in standardized units for conceptual comparison. The relative stress index for each variable was calculated individually and used as input for the total stress index (TS). In this context, ‘stress’ is defined as the departure from reference conditions for a given environmental driver (e.g., deviation from pH 8.1), weighted by the relative effect size (slope) estimated from the simple linear models.

The stress related to pH (SPH) was calculated by subtracting the reference pH value (pHREF = 8.1) from the pH of each observation (pHO).

The stress related to temperature (ST) was calculated by subtracting the reference temperature value (TREF = 2.2°C) from the temperature for each observation and dividing the result by the calculated contribution value for the temperature, according to the following formula:

ST = [(TO – TREF)/ CT]

Where: ST = stress related to temperature, TO = temperature in each observation, TREF = reference temperature (2.2°C) and CT = contribution due to the temperature.

The salinity-related stress was calculated by subtracting the reference salinity value (SREF = 34) from the salinity for each observation and dividing the result by the calculated contribution value for the salinity, according to the following formula:

SS = - [(SO – SREF)/ CS]

Where: SS = stress related to salinity, SO = salinity in each observation, SREF = reference salinity (34) and CS = contribution due to the salinity

Finally, the total stress (TS) was calculated additively, according to the following formula:

TS = SpH + ST + SS

Where: TS = Total stress; SpH = stress related to pH; ST = Stress related to temperature and SS = stress related to salinity

The dataset we present here contains all these calculations.

File List Hobbs_Hilborn_supplement1.txt -- Contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and prey dependent model predictions and calculations. Hobbs_Hilborn_supplement2.txt -- Contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and ratio dependent model predictions and calculations. Hobbs_Hilborn_supplement3.txt -- Contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and predator dependent model predictions and calculations. Hobbs_Hilborn_supplement4.txt -- Contains estimates of model selection statistics for the prey dependent, ratio dependent, and predator dependent models. Hobbs_Hilborn_supplement.xls -- A spreadsheet showing details of the computation. The data files listed above can be downloaded into a spreadsheet of your choice. Description Hobbs_Hilborn_supplement1.txt contains data collected at Isle Royal National Park (Michigan, USA) during the winter (January and February) from 1971–2001 and prey dependent model predictions and calculations. The file is tab delimited and contains 8 columns and 94 rows. The columns correspond to the variables listed below, and each row corresponds to a yearly observation, prey dependent model prediction, or calculation. Hobbs_Hilborn_supplement1 -- TABLE: Please see in attached file. -- To make sure the file was downloaded properly, compare the column sums to the following figures: -- TABLE: Please see in attached file. -- There are also calculations based on the data that are indexed in a column to their left (2 columns, 7 rows). These indices and the calculations they index are: -- TABLE: Please see in attached file. -- Hobbs_Hilborn_supplement2.txt contains data collected at IsleRoyalNational Park (Michigan, USA) during the winter (January and February) from 1971–2001 and ratio dependent model predictions and calculations. The file is tab delimited and contains 9 columns and 94 rows. The columns correspond to the variables listed below, and each row corresponds to a yearly observation, ratio dependent model prediction, or calculation. Hobbs_Hilborn_supplement2 -- TABLE: Please see in attached file. -- To make sure the file was downloaded properly, compare the column sums to the following figures: -- TABLE: Please see in attached file. -- There are also calculations based on the data that are indexed in a column to their left (2 columns, 7 rows). These indices and the calculations they index are: -- TABLE: Please see in attached file. -- Hobbs_Hilborn_supplement3.txt contains data collected at IsleRoyalNational Park (Michigan, USA) during the winter (January and February) from 1971–2001 and predator dependent model predictions or calculations. The file is tab delimited and contains 10 columns and 94 rows. The columns correspond to the variables listed below, and each row corresponds to a yearly observation, predator dependent model prediction, or calculation. Hobbs_Hilborn_supplement3 -- TABLE: Please see in attached file. -- To make sure the file was downloaded properly, compare the column sums to the following figures: -- TABLE: Please see in attached file. -- There are also calculations based on the data that are indexed in a column to their left (2 columns, 7 rows. These indices and the calculations they index are: -- TABLE: Please see in attached file. -- Hobbs_Hilborn_supplement4.txt contains estimates of model selection statistics for the prey dependent, ratio dependent, and predator dependent models. The file is tab delimited and contains 5 columns and 4 rows. The columns correspond to the variables listed below, and each row corresponds to a model type. Hobbs_Hilborn_supplement4 -- TABLE: Please see in attached file. -- To make sure the file was downloaded properly, compare the column sums to the following figures: -- TABLE: Please see in attached file. -- Instructions To reproduce the results presented in the paper, see the descriptions of the indexed calculations shown above. To obtain the parameter estimates, use a nonlinear optimization routine to find the values of model parameters that maximize the sum of the log likelihoods.

a, , and are reflectance of blue, red, and NIR band in the HJ-1A CCD optical image, respectively.Calculation for vegetation indices a.

This dataset presents the numerical fragility indicators calculated from the formulas developed by the Mednum. All the variables taken into account to calculate the scores are present in this dataset. Calculations are done via python’s Jupyter Notebook.

Yearly citation counts for the publication titled "GPU-based fast gamma index calculation".

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.

We used sequences to answer our third objective relating to the genetic diversity of bullfrogs in New Mexico. We used the following R code and input files to calculate genetic diversity indices including haplotype and nucleotide diversity to compare New Mexico diversity of bullfrog populations by site to the native range.

(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).