The global distribution hydroclimatic seasonality was evaluated using seven existing metrics (Maps_seasonality_indices.pdf). Each .txt file contains the metric values calculated from the mean monthly climatological precipitation (P) and/or potential evapotranspiration (PET) data within the CRU TS dataset version 4.03 (Harris et al. 2014), at 0.5 degree grid resolution. More information on how each metric value was calculated can be found in the included Table_seasonality_indices.pdf. The Python code simulate_sinusoid_P_PET.py produces different synthetic variations of sinusoidal seasonal distributions of P and PET that vary in terms of their relative amplitudes and phases, and calculates their associated asynchronicity index values.

1. Asynchro.txt: the asynchronicity index proposed in Feng et al. (2019).
2. dCentroid: the centroid difference (in months) between the seasonal precipitation and PET pmfs
3. WalshS.txt: seasonality index of Walsh & Lawler (1981)
4. MillyS.txt: seasonality index of Milly (1994)
5. dP*.txt: seasonality index of Woods (2009)
6. SI.txt: seasonality index of Feng et al. (2013)
7. Imr.txt: seasonality index of Knoben et al. (2018)

References: Feng, X., Porporato, A., & Rodriguez-Iturbe, I. (2013). Changes in rainfall seasonality in the tropics. Nature Climate Change, 3(9), 811–815. https://doi.org/10.1038/nclimate1907

Feng, X. Thompson, S.E., Woods, R., & Porporato, I. (2019). Quantifying asynchronicity of precipitation and potential evapotranspiration in Mediterranean climates. Geophysical Research Letters.

Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711

Knoben, W. J. M., Woods, R. A., & Freer, J. E. (2018). A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data. Water Resources Research, 54(7), 5088–5109. https://doi.org/10.1029/2018WR022913

Milly, P. C. D. C. D. (1994). Climate, interseasonal storage of soil water, and the annual water balance. Advances in Water Resources, 17(1–2), 19–24. https://doi.org/10.1016/0309-1708(94)90020-5

Walsh, R. P. D., & Lawler, D. M. (1981). Rainfall Seasonality: Description, Spatial Patterns and Change Through Time. Weather, 36(7), 201–208. https://doi.org/10.1002/j.1477-8696.1981.tb05400.x

Woods, R. A. (2009). Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks. Advances in Water Resources, 32(10), 1465–1481. https://doi.org/10.1016/j.advwatres.2009.06.011

About Transportation Services Index

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 our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

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.

Annual rainfall seasonality is an index derived from two ratios. The ratio of warm (Oct-Nov-Dec-Jan-Feb-Mar) to cool (Apr-May-Jun-Jul-Aug-Sep) season log-rainfall totals (minus 1) are assigned positive values when rainfall in the warm season is greater than rainfall in the cool season. The ratio of cool to warm season log-rainfall totals (plus 1) are assigned negative values when rainfall in the cool season is greater than rainfall in the warm season.

IPI: SEA: General-Purpose, Production&Business Use Machinery(V2010) data was reported at 102.910 2015=100 in Dec 2023. This records an increase from the previous number of 101.730 2015=100 for Nov 2023. IPI: SEA: General-Purpose, Production&Business Use Machinery(V2010) data is updated monthly, averaging 98.740 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 126.650 2015=100 in Mar 2021 and a record low of 87.510 2015=100 in Aug 2020. IPI: SEA: General-Purpose, Production&Business Use Machinery(V2010) 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.

IPI: SEA: Mfg: FT: Milling & Conditioning Powder data was reported at 108.800 2015=100 in Dec 2023. This records an increase from the previous number of 105.610 2015=100 for Nov 2023. IPI: SEA: Mfg: FT: Milling & Conditioning Powder data is updated monthly, averaging 98.340 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 110.940 2015=100 in Dec 2021 and a record low of 89.780 2015=100 in Jan 2021. IPI: SEA: Mfg: FT: Milling & Conditioning Powder 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.

IPI: SEA: Mfg: CSC: Cement & Cement Products data was reported at 107.760 2015=100 in Dec 2023. This records an increase from the previous number of 107.710 2015=100 for Nov 2023. IPI: SEA: Mfg: CSC: Cement & Cement Products data is updated monthly, averaging 99.885 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 109.290 2015=100 in Oct 2021 and a record low of 91.690 2015=100 in Jan 2019. IPI: SEA: Mfg: CSC: Cement & Cement Products 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.

IPI: SEA: Mfg: Oth: Other Products data was reported at 96.240 2015=100 in Dec 2023. This records a decrease from the previous number of 100.960 2015=100 for Nov 2023. IPI: SEA: Mfg: Oth: Other Products data is updated monthly, averaging 100.945 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 108.560 2015=100 in Mar 2021 and a record low of 86.990 2015=100 in Aug 2020. IPI: SEA: Mfg: Oth: Other Products 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.

Seasonal concentration index and Herfindahl index of public attention index in China.

IPI: SEA: Mfg: FT: Noodles data was reported at 102.840 2015=100 in Dec 2023. This records an increase from the previous number of 100.940 2015=100 for Nov 2023. IPI: SEA: Mfg: FT: Noodles data is updated monthly, averaging 99.915 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 109.090 2015=100 in Apr 2020 and a record low of 89.840 2015=100 in Aug 2014. IPI: SEA: Mfg: FT: Noodles 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.

The water supply seasonal variability is a normalized indicator of the variation in water supply between months of the year. Estimations are given for the year 2014. Seasonal variability is calculated as the standard deviation of monthly total blue water divided by the mean of total blue water calculated using the monthly mean. The indicator was created by the World Resources Institute (WRI) and ranges from 0-5, where 0 is lowest and 5 is highest. Values represent the "All-sector" indicator, and have been rounded to the nearest tenth by AQUASTAT.For more information, see WRI original analysis here: wri.org/publication/aqueduct-country-river-basin-rankings.Visit the FAO Aquastat website: http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en

Table of INEBase Series corrected for seasonal and calendar effects General index, per sectors and activity branches. Monthly. National. Services Sector Production Index

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

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 July of 2025.

Table of INEBase Series adjusted for seasonal and calendar effects General Index, by sectors and branch of activities. Monthly. National. Services Sector Activity Indicators

This dataset is a seasonal time-series 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 provided in a separate dataset for each time step. Spring: March-April_May (_MAM) Summer: June-July-August (_JJA) Autumn: September-October-November (_SON) Winter: December-January-February (_DJF) Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch)

Graph and download economic data for Harmonized Index of Consumer Prices: Overall Index Excluding Seasonal Food for Italy (00XSEAITM086NEST) from Dec 1999 to Jun 2025 about Italy, harmonized, food, CPI, price index, indexes, and price.

Graph and download economic data for Harmonized Index of Consumer Prices: Seasonal Food for Switzerland (SEAS00CHM086NEST) from Dec 2004 to Jun 2025 about Switzerland, harmonized, food, CPI, price index, indexes, and price.

Abstract This study aimed to characterize Brachiaria brizantha cv. Marandu seasonal production (seasonality) and its variation (climate risk) yearlong throughout Brazil. Data from weather stations in Brazil (1963-2009), were associated with an empirical herbage accumulation rate (HAR; kg DM ha-1 day-1) model which considers growing degree-days adjusted by a drought attenuation index. Simulations were performed under 20, 40, 60 and 100 mm of soil water holding capacities (SWHCs). HAR’s means and standard deviations were calculated for the seasons of the year. Thereafter, cluster analysis and calculations were performed to gather similar weather stations and characterize seasonality and climate risk indexes. Cluster analysis resulted in four Groups per SWHC. The north of Brazil (Group 1) presented the lowest seasonality and climate risk indexes and low need for precautions. In the middle west (Group 2), the seasonality index ranged from medium-high to high. Winter and Summer presented the lowest and highest production, respectively. In the south of Brazil, some regions in the southeast and northeast (Group 3), Winter presented the lowest production and highest climate risk index, probably due to low temperatures. The northeast (Group 4) presented a seasonality index that ranged from medium-high to very high and low productions.

IPI: SEA: Mfg: IE: Wired Communication Equipment data was reported at 109.020 2015=100 in Dec 2023. This records an increase from the previous number of 97.340 2015=100 for Nov 2023. IPI: SEA: Mfg: IE: Wired Communication Equipment data is updated monthly, averaging 95.940 2015=100 from Jan 2013 (Median) to Dec 2023, with 132 observations. The data reached an all-time high of 180.450 2015=100 in Mar 2015 and a record low of 72.390 2015=100 in May 2019. IPI: SEA: Mfg: IE: Wired Communication Equipment 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.

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.