This dataset provides values for INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

United States Import Value Index data was reported at 126.779 2015=100 in 2021. This records an increase from the previous number of 103.958 2015=100 for 2020. United States Import Value Index data is updated yearly, averaging 51.384 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 126.779 2015=100 in 2021 and a record low of 11.319 2015=100 in 1982. United States Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

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).

Graph and download economic data for ICE BofA 7-10 Year US Corporate Index Total Return Index Value (BAMLCC4A0710YTRIV) from 1992-06-30 to 2025-07-11 about 7 to 10 years, return, corporate, indexes, and USA.

CRB Index fell to 373.31 Index Points on July 14, 2025, down 0.01% from the previous day. Over the past month, CRB Index's price has fallen 1.86%, but it is still 10.05% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for ICE BofA 1-3 Year US Corporate Index Total Return Index Value (BAMLCC1A013YTRIV) from 1975-12-31 to 2025-07-11 about 1 to 3 years, return, indexes, and USA.

The Standardized Precipitation Index (SPI) was generated for certain Environment Canada long-term climate stations in Ontario.

The SPI quantifies the precipitation deficit and surplus for multiple time scales, including:

one month three months six months nine months 12 months 24 months

You can use the SPI to study the impact of dry and wet weather conditions to create comprehensive water management approaches.

The SPI data package is distributed as a Microsoft Access Geodatabase.

This is a legacy dataset that we no longer maintain or support.

The documents referenced in this record may contain URLs (links) that were valid when published, but now link to sites or pages that no longer exist.

Additional Documentation

Standardized Precipitation Index - User Guide (PDF)

Status Completed: production of the data has been completed

Maintenance and Update Frequency
Not planned: there are no plans to update the data

Contact
Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

Vietnam: Security threats index, 0 (low) - 10 (high): The latest value from 2024 is 3.2 index points, a decline from 3.5 index points in 2023. In comparison, the world average is 4.87 index points, based on data from 176 countries. Historically, the average for Vietnam from 2007 to 2024 is 4.99 index points. The minimum value, 3.2 index points, was reached in 2024 while the maximum of 7.4 index points was recorded in 2007.

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.

United States New York Stock Exchange: Index: US 100 Index data was reported at 16,491.357 NA in Apr 2025. This records a decrease from the previous number of 16,852.600 NA for Mar 2025. United States New York Stock Exchange: Index: US 100 Index data is updated monthly, averaging 10,169.600 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 17,440.815 NA in Feb 2025 and a record low of 5,695.000 NA in May 2012. United States New York Stock Exchange: Index: US 100 Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

The real-time index database market is experiencing robust growth, driven by the increasing demand for immediate insights from large volumes of data across diverse sectors. The market's expansion is fueled by the proliferation of IoT devices generating massive real-time data streams, the need for faster decision-making in competitive environments, and the rise of sophisticated analytics applications requiring rapid data access. Cloud-based solutions dominate the market due to their scalability, cost-effectiveness, and ease of deployment, attracting both individual users and large enterprises. However, concerns around data security and latency in cloud-based systems present some restraints. The on-premises segment, while smaller, continues to cater to businesses with stringent data sovereignty requirements or those managing exceptionally sensitive information. Key players like Elastic, Amazon Web Services, Apache Solr, Splunk, and Microsoft are shaping the market landscape through continuous innovation and competitive offerings. Geographic distribution reflects the concentration of technological infrastructure and data generation, with North America and Europe currently leading the market, followed by the Asia-Pacific region showing significant potential for future growth. The market's Compound Annual Growth Rate (CAGR) suggests a consistent upward trajectory, indicating continued investment and market expansion throughout the forecast period. The competitive dynamics are marked by a mix of established players and emerging entrants. Established players leverage their existing infrastructure and customer bases, while new entrants focus on niche areas and innovative solutions. The market is also witnessing increased adoption of hybrid models combining cloud and on-premises solutions to balance cost-efficiency, security, and performance. Future growth will depend on technological advancements, particularly in areas like distributed ledger technology and edge computing, which will enhance the real-time capabilities and scalability of index databases. Furthermore, the increasing focus on data governance and regulatory compliance will also influence market adoption and shape the development of future solutions. The market is anticipated to witness a sustained period of growth, fueled by the ever-growing demand for real-time data analytics and insights across various sectors and regions.

In April 2025, the Industrial Production Index (IPI) came to a value of ***** in the United States. This reflects no significant change from the previous month.The IPI was created by the Federal Reserve to measure the performance of industrial production - manufacturing, mining, electric and gas industries - in the United States relative to a base year. A value of over *** shows positive production performance, while a value below *** indicates an industrial production performance below the standards of the base year.

Licensed under: Creative Commons Attribution 4.0

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

The Office of Policy and Management (OPM) prepares the Public Investment Community (PIC) index not later than July 15 annually, pursuant to §7-545 of the Connecticut General Statutes (CGS). The PIC index measures the relative wealth and need of Connecticut’s towns by ranking them in descending order by their cumulative point allocations for: (1) per capita income; (2) adjusted equalized net grand list per capita; (3) equalized mill rate; (4) per capita aid to children receiving Temporary Family Assistance program benefits; and (5) unemployment rate. Pursuant to CGS §7-545 the PIC index includes each town that has a cumulative point ranking in the top quartile of the PIC Index (i.e. the 42 towns with the highest number of points). When a town’s ranking falls below the top quartile in a given fiscal year, the town's designation as a Public Investment Community continues for that year and the following four fiscal years. As a result, the PIC index includes certain towns carried over from previous fiscal years. The PIC index determines eligibility for several financial assistance programs that various agencies administer, including: -Urban Action Bond Assistance -Small Town Economic Assistance Program -Community Economic Development Program -Residential Mortgage Guarantee Program -Education Cost Sharing -Malpractice Insurance Purchase Program -Connecticut Manufacturing Innovation Fund -Enterprise Corridor Zone Designation Most of the towns included on the PIC index are eligible to elect for assistance under the Small Town Economic Assistance Program (STEAP) in lieu of Urban Action Bond assistance, pursuant to CGS §4-66g(b). An eligible town’s legislative body (or its board of selectmen if the town’s legislative body is the town meeting) must vote to choose STEAP assistance and the town must notify OPM following the vote. STEAP election is valid for four years and the statute allows extensions for additional four-year periods. STEAP election is not available for Ansonia, Bridgeport, Bristol, Danbury, East Hartford, Enfield, Groton, Hartford, Killingly, Manchester, Meriden, Middletown, New Britain, New Haven, New London, Norwalk, Norwich, Stamford, Torrington, Vernon, Waterbury, West Hartford, West Haven, and Windham. Pursuant to CGS §7-545, the following municipalities are also Public Investment Communities: Groton Montville Preston Scotland Thomaston Thompson Voluntown Wethersfield

The World Press Freedom Index, compiled by Reporters Without Borders (RSF), assesses press freedom in 180 countries and territories. It defines press freedom as journalists’ ability to report independently without political, economic, legal, or social interference and threats to their safety. The Index evaluates five key indicators: political context, legal framework, economic conditions, sociocultural environment, and journalist safety. It reflects the state of press freedom during the previous calendar year but may be updated to account for significant recent events, such as conflicts, coups, or major attacks on journalists.

United States Economic Optimism Index data was reported at 49.100 NA in Apr 2025. This records a decrease from the previous number of 49.800 NA for Mar 2025. United States Economic Optimism Index data is updated monthly, averaging 48.600 NA from Feb 2001 (Median) to Apr 2025, with 291 observations. The data reached an all-time high of 62.900 NA in Mar 2002 and a record low of 35.800 NA in Aug 2011. United States Economic Optimism Index data remains active status in CEIC and is reported by TechnoMetrica Institute of Policy and Politics. The data is categorized under Global Database’s United States – Table US.S027: Economic Optimism Index. [COVID-19-IMPACT]

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.

In April 2020, the global consumer confidence index of ** countries worldwide dropped to **** following the outbreak of the COVID-19 pandemic. It then slowly increased until July 2021, when it reached an index score of ****. Global consumer confidence dropped in the latter half of 2022 following rising inflation rates, but has been increasing since November that year.