The endpoints selected for evaluation of the HIINT formula were percent relative liver weight of mice (PcLiv) and the logarithm of ALT [Log(ALT)], where the log transformation was used to help stabilize the increases in variance with dose found in the ALT dataset.

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-month change in the Adjusted price index based on monthly adjusted consumer expenditure basket weights created by Statistics Canada, in partnership with the Bank of Canada. The Adjusted price index has been updated to incorporate the 2020 basket weights and is now based on a Similarity-linked Fisher price index formula. The expenditure data covers all goods and services in the Consumer Price Index.

Japan Exports Unit Value Index: Fisher Formula (FF): Total data was reported at 101.150 2015=100 in Sep 2018. This records a decrease from the previous number of 101.660 2015=100 for Aug 2018. Japan Exports Unit Value Index: Fisher Formula (FF): Total data is updated monthly, averaging 84.770 2015=100 from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 103.200 2015=100 in Dec 2014 and a record low of 71.250 2015=100 in Oct 2003. Japan Exports Unit Value Index: Fisher Formula (FF): Total data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA052: Exports Unit Value Index: 2015=100.

United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds was 258.48200 Index 1982=100 in March 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 301.55000 in September of 2022 and a record low of 44.20000 in February of 1962. 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 July 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

El Salvador SV: Wholesale Price Index data was reported at 108.446 2010=100 in 2016. This records a decrease from the previous number of 111.345 2010=100 for 2015. El Salvador SV: Wholesale Price Index data is updated yearly, averaging 42.663 2010=100 from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 114.438 2010=100 in 2011 and a record low of 5.573 2010=100 in 1962. El Salvador SV: Wholesale Price Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank.WDI: Inflation. Wholesale price index refers to a mix of agricultural and industrial goods at various stages of production and distribution, including import duties. The Laspeyres formula is generally used.; ; International Monetary Fund, International Financial Statistics and data files.; ;

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

This dataset is an annual time-serie of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Water Index (NDWI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDWI quantifies plant water content by measuring the difference between Near-Infrared (NIR) and Short Wave Infrared (SWIR) (or Green) channels using this generic formula: (NIR - SWIR) / (NIR + SWIR) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 2) / (Band 4 + Band 2). Landsat 8, NDVI = (Band 5 – Band 3) / (Band 5 + Band 3). NDWI values ranges from -1 to +1. NDWI is a good proxy for plant water stress and therefore useful for drought monitoring and early warning. NDWI is sometimes alos refered as Normalized Difference Moisture Index (NDMI) Standard Deviation is also provided for each time step. Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch)

Calculation of K-index.

Japan Imports Unit Value Index: FF: Fisher Formula (FF): Total data was reported at 101.170 2015=100 in Jul 2018. This records an increase from the previous number of 98.860 2015=100 for Jun 2018. Japan Imports Unit Value Index: FF: Fisher Formula (FF): Total data is updated monthly, averaging 87.550 2015=100 from Jan 2003 (Median) to Jul 2018, with 187 observations. The data reached an all-time high of 116.250 2015=100 in Aug 2008 and a record low of 61.000 2015=100 in Nov 2003. Japan Imports Unit Value Index: FF: Fisher Formula (FF): Total data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA062: Imports Unit Value Index: 2015=100.

This dataset contains the World Average Degree Days Database for the period 1964-2013. Follow datasource.kapsarc.org for timely data to advance energy economics research.*

Summary_64-13_freq=1D Average Degree Days of various indices for respective countries for the period 1964-2013, converted to a 1 day frequency

Summary_64-13_freq=6hrs Average Degree Days of various indices for respective countries for the period 1964-2013, calculated at 6 hrs frequency

T2m.hdd.18C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

T2m.cdd.18C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18°C and frequency of 6 hrs

t2m.hdd.15.6C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.hdd.18.3C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.hdd.21.1C Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.cdd.15.6C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=15.6°C and frequency of 6 hrs

t2m.cdd.18.3C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=18.3°C and frequency of 6 hrs

t2m.cdd.21.1C Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=21.1°C and frequency of 6 hrs

t2m.hdd.60F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.hdd.65F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.hdd.70F Calculation of Heating Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

t2m.cdd.60F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=60°F and frequency of 6 hrs

t2m.cdd.65F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=65°F and frequency of 6 hrs

t2m.cdd.70F Calculation of Cooling Degree Days using plain temperature at 2 m elevation at Tref=70°F and frequency of 6 hrs

HI.hdd.57.56F Calculation of Heating Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.hdd.63.08F Calculation of Heating Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.hdd.68.58F Calculation of Heating Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HI.cdd.57.56F Calculation of Cooling Degree Days using the Heat Index at Tref=57.56°F and frequency of 6 hrs

HI.cdd.63.08F Calculation of Cooling Degree Days using the Heat Index at Tref=63.08°F and frequency of 6 hrs

HI.cdd.68.58F Calculation of Cooling Degree Days using the Heat Index at Tref=68.58°F and frequency of 6 hrs

HUM.hdd.13.98C Calculation of Heating Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.hdd.17.4C Calculation of Heating Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.hdd.21.09C Calculation of Heating Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

HUM.cdd.13.98C Calculation of Cooling Degree Days using the Humidex at Tref=13.98°C and frequency of 6 hrs

HUM.cdd.17.4C Calculation of Cooling Degree Days using the Humidex at Tref=17.40°C and frequency of 6 hrs

HUM.cdd.21.09C Calculation of Cooling Degree Days using the Humidex at Tref=21.09°C and frequency of 6 hrs

ESI.hdd.12.6C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.hdd.14.9C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.hdd.17.2C Calculation of Heating Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

ESI.cdd.12.6C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=12.6°C and frequency of 6 hrs

ESI.cdd.14.9C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=14.9°C and frequency of 6 hrs

ESI.cdd.17.2C Calculation of Cooling Degree Days using the Environmental Stress Index at Tref=17.2°C and frequency of 6 hrs

Note:

Divide Degree Days by 4 to convert from 6 hrs to daily frequency

(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 PPI: Weights: PO: Formula Feeds data was reported at 0.413 % in 2024. This records a decrease from the previous number of 0.432 % for 2023. United States PPI: Weights: PO: Formula Feeds data is updated yearly, averaging 0.381 % from Dec 2007 (Median) to 2024, with 18 observations. The data reached an all-time high of 0.469 % in 2022 and a record low of 0.266 % in 2007. United States PPI: Weights: PO: Formula Feeds data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I068: Producer Price Index: by Commodities: Weights.

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.

1) This data is the aridity index data calculated based on the latest simulation results of 22 cmip6 coupled global climate models; 2) The calculation formula is p / PET (ratio of precipitation to potential evapotranspiration), and the calculation of pet is based on PM formula; 3) The monthly data of the Great Lakes region of Central Asia from January 1900 to December 2100, including ssp2-4.5 and ssp5-8.5, with a resolution of 1 degree * 1 degree; 4) The data can be used to analyze the distribution and evolution of dry and wet pattern in the Great Lakes region of Central Asia under medium and high emission scenarios in the future. The data has been converted into 3-mongth running means.

Import Unit Value Index: Fisher Formula (FF) data was reported at 165.810 2020=100 in Mar 2025. This records a decrease from the previous number of 175.290 2020=100 for Feb 2025. Import Unit Value Index: Fisher Formula (FF) data is updated monthly, averaging 105.330 2020=100 from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 186.460 2020=100 in Oct 2022 and a record low of 68.810 2020=100 in Nov 2003. Import Unit Value Index: Fisher Formula (FF) data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Japan – Table JP.JA069: Imports Unit Value Index: 2020=100.

The apex index represents a combined relief parameter of relative slope position and slope inclination. It is primarily used to determine vertex areas (relatively shallow locations). As a side effect, average values of the apex index usually indicate slopes or flattening (e.g. terraces, geest plates) Medium to high values indicate valley floors and very high values indicate steep slopes in a relative low position (e.g. terrace embankments and steep notched valleys). It is calculated using the following formula: Vertex index = relHP + N where: relHP = relative slope position (inverted) N = slope inclination (inclinations > 60° -> = 60°, exponent = 0.4, normalized to 0.0 to 1.0) By including the slope slope, the relief parameter relative slope position is modified in such a way that flattenings in the relative top position of the relief have very low values and there is a usually abrupt increase in the apex area index at the transition to the slopes. Definition and calculation method: KÖTHE (2007), realized by SAGA module of scilands GmbH and SAGA module "Grid Calculator".

We provide evidence on the effect of elementary index choice on inflation measurement in the euro area. Using scanner data for 15,844 individual items from 42 product categories and 10 euro area countries, we compute product category level elementary price indexes using eight different elementary index formulas. Measured inflation outcomes of the different index formulas are compared with the Fisher ideal index to quantify elementary index bias. We have three main findings. First, elementary index bias is quite variable across product categories, countries and index formulas. Second, a comparison of elementary index formulas with and without expenditure weights shows that a shift from price only indexes to expenditure weighted indexes would entail at the product level multiple percentage points differences in measured price changes. And finally, we show that elementary index bias is quantitatively more important than upper level substitution bias.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.