Graph and download economic data for Chain-Type Quantity Index for Real GDP: Information (51) in Alabama (ALINFOQGSP) from 1997 to 2024 about quantity index, information, AL, GSP, private industries, private, industry, GDP, and USA.

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007. From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank. From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here: http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter. Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

China Index: Shenzhen Stock Exchange: Information and Technology data was reported at 1,875.790 03Apr1991=100 in 14 May 2025. This records an increase from the previous number of 1,859.680 03Apr1991=100 for 13 May 2025. China Index: Shenzhen Stock Exchange: Information and Technology data is updated daily, averaging 1,633.880 03Apr1991=100 from Jul 2001 (Median) to 14 May 2025, with 5786 observations. The data reached an all-time high of 5,045.278 03Apr1991=100 in 03 Jun 2015 and a record low of 467.812 03Apr1991=100 in 03 Dec 2012. China Index: Shenzhen Stock Exchange: Information and Technology data remains active status in CEIC and is reported by Shenzhen Stock Exchange. The data is categorized under High Frequency Database’s Financial and Futures Market – Table CN.ZA: Shenzhen Stock Exchange: Indices: Daily.

Chain-Type Quantity Index for Real GDP: Information (NAICS 51) in the United States was 167.91700 Index 2009=100 in October of 2024, according to the United States Federal Reserve. Historically, Chain-Type Quantity Index for Real GDP: Information (NAICS 51) in the United States reached a record high of 167.91700 in October of 2024 and a record low of 50.92200 in January of 2005. Trading Economics provides the current actual value, an historical data chart and related indicators for Chain-Type Quantity Index for Real GDP: Information (NAICS 51) in the United States - last updated from the United States Federal Reserve on July of 2025.

This dataset denotes values of the School Proficiency Index. The index illustrates school-level data to describe neighborhoods relative to nearby school performance. Specifically, the data is on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools.

The value of the DJIA index amounted to ********* at the end of March 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Chain-Type Quantity Index for Real GDP: Data Processing, Hosting, and Other Information Services (NAICS 518, 519) in Vermont was 313.98000 Index 2009=100 in January of 2023, according to the United States Federal Reserve. Historically, Chain-Type Quantity Index for Real GDP: Data Processing, Hosting, and Other Information Services (NAICS 518, 519) in Vermont reached a record high of 313.98000 in January of 2023 and a record low of 22.60800 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Chain-Type Quantity Index for Real GDP: Data Processing, Hosting, and Other Information Services (NAICS 518, 519) in Vermont - last updated from the United States Federal Reserve on July of 2025.

Oman OM: Depth of Credit Information Index: 0=Low To 8=High data was reported at 6.000 NA in 2017. This stayed constant from the previous number of 6.000 NA for 2016. Oman OM: Depth of Credit Information Index: 0=Low To 8=High data is updated yearly, averaging 6.000 NA from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 6.000 NA in 2017 and a record low of 6.000 NA in 2017. Oman OM: Depth of Credit Information Index: 0=Low To 8=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Oman – Table OM.World Bank.WDI: Business Environment. Depth of credit information index measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a public registry or a private bureau, to facilitate lending decisions.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Graph and download economic data for Producer Price Index by Industry: Information (PCUAINFOAINFO) from Dec 2006 to May 2025 about information, PPI, industry, inflation, price index, indexes, price, and USA.

Israel Wage Index: Information and Communication data was reported at 162.192 2011=100 in Aug 2018. This records a decrease from the previous number of 184.606 2011=100 for Jul 2018. Israel Wage Index: Information and Communication data is updated monthly, averaging 125.260 2011=100 from Jan 2012 (Median) to Aug 2018, with 80 observations. The data reached an all-time high of 184.606 2011=100 in Jul 2018 and a record low of 99.925 2011=100 in May 2012. Israel Wage Index: Information and Communication data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G038: Wage Index.

The testing dataset used at TRECVID for the DSDI task in 2020-2022.The dataset includes public videos, ground truth and features of the DSDI task. As the task is continuing, the dataset will be continually updated.There are 32 features across 5 main categories (Environment, Vehicles, Water, Infrastructure, Damage). All videos are airborne low altitude from natural disaster events.

United States - Consumer Price Index for All Urban Consumers: Information Technology, Hardware and Services in U.S. City Average was 6.77800 Index Dec 1988=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Information Technology, Hardware and Services in U.S. City Average reached a record high of 13.00000 in January of 2006 and a record low of 6.72400 in December of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Information Technology, Hardware and Services in U.S. City Average - last updated from the United States Federal Reserve on July of 2025.

Leaf Area Index (LAI) is a fundamental vegetation structural variable that drives energy and mass exchanges between the plant and the atmosphere. Moderate-resolution (300m – 7km) global LAI data products have been widely applied to track global vegetation changes, drive Earth system models, monitor crop growth and productivity, etc. Yet, cutting-edge applications in climate adaptation, hydrology, and sustainable agriculture require LAI information at higher spatial resolution (< 100m) to model and understand heterogeneous landscapes. This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine. Each record/sample/row includes a MODIS LAI value, corresponding Landsat surface reflectance in green, red, NIR, SWIR1 bands, a land cover (biome) type, geographic location, and other auxiliary information. Each sample represents a MODIS LAI pixel (500m) within which a single biome type dominates 90% of the area. The spatial homogeneity of the samples was further controlled by a screening process based on the coefficient of variation of the Landsat surface reflectance. In total, there are approximately 1.6 million samples, stratified by biome, Landsat sensor, and saturation status from the MODIS LAI algorithm. This dataset can be used to train machine learning models and generate LAI maps for Landsat 5, 7, 8 surface reflectance images within CONUS. Detailed information on the sample generation and quality control can be found in the related journal article. Resources in this dataset:Resource Title: README. File Name: LAI_train_samples_CONUS_README.txtResource Description: Description and metadata of the main datasetResource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab Resource Title: LAI_training_samples_CONUS. File Name: LAI_train_samples_CONUS_v0.1.1.csvResource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang (kangyanghui@gmail.com)
Column description

UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE
Landsat_ID: Landsat image ID Date: Landsat image date in "YYYYMMDD" Latitude: Latitude (WGS84) of the MODIS LAI pixel center Longitude: Longitude (WGS84) of the MODIS LAI pixel center MODIS_LAI: MODIS LAI value in "m2/m2" MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2" MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation NLCD_class: Majority class code from the National Land Cover Dataset (NLCD) NLCD_frequency: Percentage of the area cover by the majority class from NLCD Biome: Biome type code mapped from NLCD (see below for more information) Blue: Landsat surface reflectance in the blue band Green: Landsat surface reflectance in the green band Red: Landsat surface reflectance in the red band Nir: Landsat surface reflectance in the near infrared band Swir1: Landsat surface reflectance in the shortwave infrared 1 band Swir2: Landsat surface reflectance in the shortwave infrared 2 band Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value. Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value. NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance EVI: Enhanced Vegetation Index computed from Landsat surface reflectance NDWI: Normalized Difference Water Index computed from Landsat surface reflectance GCI: Green Chlorophyll Index = Nir/Green - 1

Biome code

1 - Deciduous Forest
2 - Evergreen Forest
3 - Mixed Forest
4 - Shrubland
5 - Grassland/Pasture
6 - Cropland
7 - Woody Wetland
8 - Herbaceous Wetland

Reference Dataset: All data was accessed through Google Earth Engine Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006 Landsat 5/7/8 Collection 1 Surface Reflectance Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey. Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. National Land Cover Dataset (NLCD) Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006 Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

Lithuania - Labour cost index: Information and communication was 11.10 % year-on-year in March of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Lithuania - Labour cost index: Information and communication - last updated from the EUROSTAT on July of 2025. Historically, Lithuania - Labour cost index: Information and communication reached a record high of 19.50 % year-on-year in December of 2022 and a record low of 6.10 % year-on-year in March of 2021.

The dataset contains the leaf area index (LAI) (m2/ m2 ) collected with the LAI-2200 C (plant canopy analyzer), that was computed with 5, 4 and 3 rings using the FV2200 software. Additionally, it has the x and y coordinates (m) of the points collected inside the plots and the time of collection (hour: min: sec). This research was collected in a field site approximately 80 km north of Manaus, in the state of Amazonas, Brasil. The campaigns were carried out in October 2017, March 2018, August 2018 and October 2018.

Purpose and brief description The consumer price index is an economic indicator whose main task is to objectively reflect the price evolution over time for a basket of goods and services purchased by households and considered representative of their consumer habits. The index does not necessarily measure the price level of this basket for a specific period of time, but rather the fluctuation between two periods, the first one acting as basis for comparison. Moreover, this difference in the price level is not measured in absolute, but in relative terms. The consumer price index can be determined as a hundred times the ratio between the observed prices of a range of goods and services at a given time and the prices of the same goods and services, observed under the same circumstances during the reference period, chosen as basis for comparison. Price observations always take place in the same regions. Since 2014, the consumer price index has been a chain index in which the weighting reference period is regularly shifted and prices and quantities are no longer compared between the current period and a fixed reference period, but the current period is compared with an intermediate period. By multiplying these short-term indices, and so creating a chain, we get a long-term series with a fixed reference period. Population Belgian private households Data collection method and possible sampling Survey technique applied using a computer, based on the use of electronic questionnaires and laptops. Frequency Monthly. Timing of publication The results are available on the penultimate working day of the reference period. Definitions Weight (CPI): The weight represents the importance of the goods and services included in the CPI in the total expenditure patterns of the households. Weights are determined based on the household budget survey. Consumer price index (CPI): The consumer price index is an economic indicator whose main task is to objectively reflect the price evolution over time for a basket of goods and services purchased by households and considered representative of their consumer habits. Health index: The health index is derived from the consumer price index and has been published since January 1994. The current value of this index is determined by removing a number of products from the consumer price index product basket, in particular alcoholic beverages (bought in a shop or consumed in a bar), tobacco products and motor fuels except for LPG. Inflation: Inflation is defined as the ratio between the value of the consumer price index of a given month and the index of the same month the year before. Therefore, inflation measures the rhythm of the evolution of the overall price level. Consumer price index without petroleum products: This index is calculated by removing the following products from the consumer price index: butane, propane, liquid fuels and motor fuels. Consumer price index without energy products: This index is calculated by removing the following products from the consumer price index: electricity, natural gas, butane, propane, liquid fuels, solid fuels and motor fuels. Smoothed index: The smoothed health index, also called smoothed index (the average value of the health indexes of the last 4 months) is used as a basis for the indexation of retirement pensions, social security benefits and some salaries and wages. Public wages and social benefits are indexed as soon as the smoothed index reaches a given value, called the central index. The smoothed index is also called moving average. In order to perform a 2% index jump (laid down in the Law of 23 April 2015 on employment promotion), the smoothed health index has been temporarily blocked at its value of March 2015 (100.66). The smoothed health index was then reduced by 2% from April 2015. When the reduced smoothed health index (also called the reference index) had increased again by 2% or in other words when it had exceeded the value of 100.66, the index was no longer blocked. It occurred in April 2016. Since April 2016 the smoothed health index is calculated in the same manner as the reference index and therefore corresponds to the arithmetical mean of the health indexes of the last 4 months multiplied by a factor of 0.98. The central index is a predetermined threshold value against which the smoothed health index is compared. If the central index is reached or exceeded, there is an indexation of the wages and salaries or benefits. This indexation is proportional to the percentage between the old and the new central index. For the public sector and social benefits, the difference between the central indices always amounts to 2 %. Therefore, a 2 % indexation is applied every time the central index is reached. There are also collective labour agreements according to which the difference between the central indices amounts to 1 % or 1.5 %. The reaching of a central index then leads to an indexation of 1 % or 1,5 %. See also: https://bosa.belgium.

Chain-Type Quantity Index for Real GDP: Information (NAICS 51) in Iowa was 149.78100 Index 2009=100 in October of 2024, according to the United States Federal Reserve. Historically, Chain-Type Quantity Index for Real GDP: Information (NAICS 51) in Iowa reached a record high of 150.23500 in October of 2023 and a record low of 72.44100 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for Chain-Type Quantity Index for Real GDP: Information (NAICS 51) in Iowa - last updated from the United States Federal Reserve on July of 2025.

As of October 2024, the industry weighting of the financial industry in the Hang Seng Composite Index (HSCI) accounted for ** percent. It was the largest subsection within the index, followed by the consumer discretionary sector. The index includes the top 95th percentile of companies at the Hong Kong Exchange.

Index price information provided by the Korea Exchange Provides operations on stock index prices, bond index prices, and derivatives index prices.

Analysis of ‘Racial and Social Equity Composite Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/42acb6e8-d61a-4349-a916-e072d62ceced on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Persons of color (weight: 1.0)
English language learner (weight: 0.5)
Foreign born (weight: 0.5)

Income below 200% of poverty level

Educational attainment less than a bachelor’s degree

No leisure-time physical activity

Diagnosed diabetes

Obesity

Mental health not good

Asthma

Low life expectancy at birth

Disability