IPI: SEA: Mfg: GPBM: Pumps & Compressors data was reported at 97.250 2015=100 in Dec 2023. This records a decrease from the previous number of 99.720 2015=100 for Nov 2023. IPI: SEA: Mfg: GPBM: Pumps & Compressors data is updated monthly, averaging 99.485 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 122.900 2015=100 in Mar 2016 and a record low of 86.150 2015=100 in Aug 2020. IPI: SEA: Mfg: GPBM: Pumps & Compressors data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.B005: Industrial Production Index: 2015=100: Seasonal Index.

Movements in the volume of production for the UK production industries: manufacturing, mining and quarrying, energy supply, and water and waste management. Figures are seasonally adjusted.

IPI: SEA: FDG: COG: ND: Electrical&InformationCommsMachineryIndustry data was reported at 103.450 2015=100 in Dec 2023. This records a decrease from the previous number of 113.440 2015=100 for Nov 2023. IPI: SEA: FDG: COG: ND: Electrical&InformationCommsMachineryIndustry data is updated monthly, averaging 100.010 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 118.580 2015=100 in Nov 2016 and a record low of 83.500 2015=100 in Jan 2021. IPI: SEA: FDG: COG: ND: Electrical&InformationCommsMachineryIndustry data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.B027: Industrial Production Index: 2015=100: By Special Classification Goods: Seasonal Index.

Graph and download economic data for Harmonized Index of Consumer Prices: Overall Index Excluding Energy and Seasonal Food for Iceland (00XESEISM086NEST) from Dec 1999 to Apr 2025 about Iceland, harmonized, energy, food, CPI, price index, indexes, and price.

Graph and download economic data for Harmonized Index of Consumer Prices: Overall Index Excluding Seasonal Food for Austria (00XSEAATM086NEST) from Dec 1999 to May 2025 about Austria, harmonized, food, CPI, price index, indexes, and price.

Harmonized Index of Consumer Prices: Overall Index Excluding Seasonal Food for Czech Republic was 154.90000 Index 2015=100 in March of 2025, according to the United States Federal Reserve. Historically, Harmonized Index of Consumer Prices: Overall Index Excluding Seasonal Food for Czech Republic reached a record high of 154.90000 in March of 2025 and a record low of 124.30000 in February of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Harmonized Index of Consumer Prices: Overall Index Excluding Seasonal Food for Czech Republic - last updated from the United States Federal Reserve on June of 2025.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

Japan IPI: SEA: Ordinary Steel (2010 Version) data was reported at 99.890 2015=100 in Dec 2023. This records a decrease from the previous number of 100.950 2015=100 for Nov 2023. Japan IPI: SEA: Ordinary Steel (2010 Version) data is updated monthly, averaging 99.425 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 107.910 2015=100 in Mar 2015 and a record low of 93.310 2015=100 in Feb 2019. Japan IPI: SEA: Ordinary Steel (2010 Version) data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.B005: Industrial Production Index: 2015=100: Seasonal Index.

Graph and download economic data for Harmonized Index of Consumer Prices: Overall Index Excluding Seasonal Food for Belgium (00XSEABEM086NEST) from Dec 1998 to May 2025 about Belgium, harmonized, food, CPI, price index, indexes, and price.

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry _location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the winter season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

An Environmental Quality Index (EQI) for all counties in the United States for the time period 2000-2005 was developed which incorporated data from five environmental domains: air, water, land, built, and socio-demographic. The EQI was developed in four parts: domain identification; data source identification and review; variable construction; and data reduction using principal components analysis (PCA). The methods applied provide a reproducible approach that capitalizes almost exclusively on publically-available data sources. The primary goal in creating the EQI is to use it as a composite environmental indicator for research on human health. A series of peer reviewed manuscripts utilized the EQI in examining health outcomes. This dataset is not publicly accessible because: This series of papers are considered Human health research - not to be loaded onto ScienceHub. It can be accessed through the following means: The EQI data can be accessed at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: EQI data, metadata, formats, and data dictionary all available at website. This dataset is associated with the following publications: Gray, C., L. Messer, K. Rappazzo, J. Jagai, S. Grabich, and D. Lobdell. The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(8): e0203301, (2018). Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018). Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.For charts and discussion on the relationship of the TSI to the economy, see the Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte). Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.

Japan IPI: SEA: Mfg: Cosmetics data was reported at 92.490 2015=100 in Dec 2023. This records a decrease from the previous number of 101.510 2015=100 for Nov 2023. Japan IPI: SEA: Mfg: Cosmetics data is updated monthly, averaging 100.320 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 113.260 2015=100 in Jul 2013 and a record low of 85.270 2015=100 in Jan 2021. Japan IPI: SEA: Mfg: Cosmetics data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.B005: Industrial Production Index: 2015=100: Seasonal Index.

Table of INEBase Series adjusted for calendar effects. Overall index, by sectors and branches of activity. Monthly. National. Services Sector Activity Indicators

Estonia - Harmonised index of consumer prices (HICP): Overall index excluding seasonal food was 160.79 points in April of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Estonia - Harmonised index of consumer prices (HICP): Overall index excluding seasonal food - last updated from the EUROSTAT on June of 2025. Historically, Estonia - Harmonised index of consumer prices (HICP): Overall index excluding seasonal food reached a record high of 160.79 points in April of 2025 and a record low of 60.30 points in December of 2000.

Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) groups (Kral et al., 2015; https://doi.org/10.5285/f1cd5e33-2633-4304-bbc2-b8d34711d902). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [1]. SPI is calculated for different accumulation periods: 1, 3, 6, 9, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. NOTE: the difference between this dataset with the previously published dataset 'Standardised Precipitation Index time series for IHU Groups (1961-2012) [SPI_IHU_groups]' (Tanguy et al., 2015; https://doi.org/10.5285/dfd59438-2170-4472-b810-bab33a83d09f), apart from the temporal extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Tanguy et al., 2014; https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). Within Historic Droughts project (grant number: NE/L01016X/1), the Met Office has digitised historic rainfall and temperature data to produce high quality historic rainfall and temperature grids, which motivated the change in the underlying data to calculate SPI. The methodology to calculate SPI is the same in the two datasets. This release supersedes the previous version, https://doi.org/10.5285/047d914f-2a65-4e9c-b191-09abf57423db, as it addresses localised issues with the source data (Met Office monthly rainfall grids) for the period 1960 to 2000. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. Full details about this dataset can be found at https://doi.org/10.5285/a01e09b6-4b40-497b-a139-9369858101b3

Portugal - Harmonised index of consumer prices (HICP): Overall index excluding seasonal food was 125.21 points in May of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Portugal - Harmonised index of consumer prices (HICP): Overall index excluding seasonal food - last updated from the EUROSTAT on June of 2025. Historically, Portugal - Harmonised index of consumer prices (HICP): Overall index excluding seasonal food reached a record high of 125.21 points in May of 2025 and a record low of 71.40 points in February of 2000.

This table presents figures on turnover and production changes in Trade and Services sector. The figures can be broken down by industry according to Statistics Netherlands' Standard Industrial Classification of all Economic Activities 2008 (SIC 2008). The change is shown both as a percentage change compared to a previous period and through index figures with 2021 as base year. Turnover and production changes are published in two forms. Firstly, as year-on-year changes where the growth is expressed relative to the same period in the previous year. These figures are presented unadjusted and calendar-adjusted. The second form represents period-on-period changes: month-on-month and quarter-on-quarter. Period-on-period changes are possible by applying seasonal adjustment. Currently, this table exclusively comprises seasonally- and calendar adjusted data pertaining to monthly records for the retail sector. For other sectors, the unadjusted monthly series only extend back to January 2021, complicating the process of conducting adjustments. The lack of sufficient historical data makes it challenging to identify consistent patterns and trends, which is crucial for accurate adjustments. Some data may not be representative of all seasonal influences occurring over a longer period, potentially leading to less reliable or even incorrect adjustments. As the unadjusted monthly series lengthens, more reliable adjustments can be made. Therefore, in the spring of 2025, the table will be expanded to include seasonally- and calendar adjusted records for other sectors, retroactively from January 2021. Data available from: January 2000 for branches within SIC division 47 and first quarter of 2005 for all other branches. Status of figures: Figures for 2024 and 2025 are provisional. The figures of a calendar year become final no later than six months after the end of that calendar year. Due to delayed response, provisional figures may still change. Changes as of June 2, 2025: Figures of industries belonging to NACE sections G, H, I, J, L, M, N and S have been added. These are figures for the period April as far as retail trade is concerned and March and the first quarter of 2025 for other industries. Figures labeled as "provisional" may have been revised. To keep the results of these index series in line with current events, a so-called base year change is carried out once every five years. In 2024, the publication of this table switched from reference year 2015 to 2021 (2021=100) and the weighting factors were updated and based on the year 2021. This table combines data from 9 separate former tables. Which tables this concerns can be found later in this table explanation under "3. Links to relevant tables and articles". When are new figures released? For monthly business statistics, figures are published as a rule 2 months after the end of the reporting month, figures for the retail trade sector and imports of new passenger cars and light commercial vehicles are published 1 month after the end of the reporting month. After publication of final results, Statistics Netherlands adjusts the figures only if major adjustments and/or corrections are necessary.