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;;

United States New York Stock Exchange: Index: US 100 Index data was reported at 18,140.503 NA in Nov 2025. This records an increase from the previous number of 17,877.968 NA for Oct 2025. United States New York Stock Exchange: Index: US 100 Index data is updated monthly, averaging 9,534.600 NA from Jan 2012 (Median) to Nov 2025, with 167 observations. The data reached an all-time high of 18,140.503 NA in Nov 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.

Dataset Name: City Happiness Index

Dataset Description:

This dataset and the related codes are entirely prepared, original, and exclusive by Emirhan BULUT. The dataset includes crucial features and measurements from various cities around the world, focusing on factors that may affect the overall happiness score of each city. By analyzing these factors, we aim to gain insights into the living conditions and satisfaction of the population in urban environments.

The dataset consists of the following features:

• City: Name of the city.
• Month: The month in which the data is recorded.
• Year: The year in which the data is recorded.
• Decibel_Level: Average noise levels in decibels, indicating the auditory comfort of the citizens.
• Traffic_Density: Level of traffic density (Low, Medium, High, Very High), which might impact citizens' daily commute and stress levels.
• Green_Space_Area: Percentage of green spaces in the city, positively contributing to the mental well-being and relaxation of the inhabitants.
• Air_Quality_Index: Index measuring the quality of air, a crucial aspect affecting citizens' health and overall satisfaction.
• Happiness_Score: The average happiness score of the city (on a 1-10 scale), representing the subjective well-being of the population.
• Cost_of_Living_Index: Index measuring the cost of living in the city (relative to a reference city), which could impact the financial satisfaction of the citizens.
• Healthcare_Index: Index measuring the quality of healthcare in the city, an essential component of the population's well-being and contentment.

With these features, the dataset aims to analyze and understand the relationship between various urban factors and the happiness of a city's population. The developed Deep Q-Network model, PIYAAI_2, is designed to learn from this data to provide accurate predictions in future scenarios. Using Reinforcement Learning, the model is expected to improve its performance over time as it learns from new data and adapts to changes in the environment.

Prominent rent growth indices often give strikingly different measurements of rent inflation. We create new indices from Bureau of Labor Statistics (BLS) rent microdata using a repeat-rent index methodology and show that this discrepancy is almost entirely explained by differences in rent growth for new tenants relative to the average rent growth for all tenants. Rent inflation for new tenants leads the official BLS rent inflation by four quarters. As rent is the largest component of the consumer price index, this has implications for our understanding of aggregate inflation dynamics and guiding monetary policy. Download NTRR and ATRR indices through 2022q3 here.

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 (indicated in the data as "grandfathered"). 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.

CRB Index rose to 378.33 Index Points on December 1, 2025, up 0.45% from the previous day. Over the past month, CRB Index's price has fallen 0.80%, but it is still 10.95% 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 December of 2025.

Graph and download economic data for Brave-Butters-Kelley Coincident Index (BBKMCOIX) from Jan 1960 to Aug 2025 about GDP, indexes, and USA.

SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data 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 school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Unlock the power of real-time data! Explore the booming real-time index database market, projected to reach $32 billion by 2033. Discover key trends, leading companies (Elastic, AWS, Splunk), and regional insights in this comprehensive market analysis.

1) Data Introduction • The AI Global Index Dataset is a comprehensive index that benchmarks 62 countries based on the level of AI investment, innovation, and implementation, including seven key indicators (human resources, infrastructure, operational environment, research, development, government strategy, commercialization) and general information by country (region, cluster, income group, political system).

2) Data Utilization (1) AI Global Index Dataset has characteristics that: • This dataset consists of a total of 13 columns with 5 categorical variables (regions, clusters, etc.) and 8 numerical variables (scores for each indicator), covering 62 countries. • The seven key indicators are classified into three pillars: △ implementation (human resources/infrastructure/operational environment) △ innovation (R&D) △ investment (government strategy/commercialization), and assess each country's overall AI ecosystem capabilities in multiple dimensions. (2) AI Global Index Dataset can be used to: • Global AI leadership pattern analysis: Correlation analysis between seven indicators can identify AI strengths and weaknesses by country and perform group comparisons by region and income level. • Machine learning-based predictive model: It can be used for data science education and application, such as country-specific index prediction through regression analysis or classification of AI development types through clustering.

This table contains data on the modified retail food environment index for California, its regions, counties, cities, towns, and census tracts. An adequate, nutritious diet is a necessity at all stages of life. Pregnant women and their developing babies, children, adolescents, adults, and older adults depend on adequate nutrition for optimum development and maintenance of health and functioning. Nutrition also plays a significant role in causing or preventing a number of illnesses, such as cardiovascular disease, some cancers, obesity, type-2 diabetes, and anemia. Peoples’ food choices and their likelihood of being overweight or obese are also influenced by their food environment: the foods available in their neighborhoods including stores, restaurants, schools, and worksites.

The modified retail food environment index table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf

The format of the modified retail food environment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

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]

NASDAQ: Index: Total Return: NASDAQ US Benchmark Consumer Staples Index data was reported at 1,470.920 NA in Apr 2025. This records a decrease from the previous number of 1,482.620 NA for Mar 2025. NASDAQ: Index: Total Return: NASDAQ US Benchmark Consumer Staples Index data is updated monthly, averaging 1,276.255 NA from Sep 2020 (Median) to Apr 2025, with 56 observations. The data reached an all-time high of 1,482.620 NA in Mar 2025 and a record low of 974.100 NA in Oct 2020. NASDAQ: Index: Total Return: NASDAQ US Benchmark Consumer Staples 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: NASDAQ: Total Return: Monthly.

France Leading Economic Index data was reported at 109.300 2016=100 in Mar 2025. This records an increase from the previous number of 108.600 2016=100 for Feb 2025. France Leading Economic Index data is updated monthly, averaging 70.300 2016=100 from Jan 1970 (Median) to Mar 2025, with 663 observations. The data reached an all-time high of 112.500 2016=100 in Jun 2023 and a record low of 58.100 2016=100 in Jul 1975. France Leading Economic Index data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s France – Table FR.The Conference Board: Leading Economic Index. [COVID-19-IMPACT]

This dataset provides air quality index (AQI) data from major cities worldwide, covering the years 2015 to 2025. It is compiled from various sources, including government monitoring stations, environmental agencies, and open APIs.

The dataset includes daily AQI values along with major pollutants such as PM2.5, PM10, NO2, SO2, CO, and O3. Additional meteorological data such as temperature, humidity, and wind speed are also included to support deeper analysis.

Dataset Features: Date: The date of AQI measurement (YYYY-MM-DD). City: Name of the city where the AQI is recorded. Country: Country of the city. AQI: The daily air quality index value. PM2.5 (µg/m³): Fine particulate matter concentration. PM10 (µg/m³): Larger particulate matter concentration. NO2 (ppb): Nitrogen dioxide concentration. SO2 (ppb): Sulfur dioxide concentration. CO (ppm): Carbon monoxide concentration. O3 (ppb): Ozone concentration. Temperature (°C): Daily average temperature. Humidity (%): Daily average humidity. Wind Speed (m/s): Daily average wind speed. Potential Use Cases: ✅ Data Science & Machine Learning: Predict air quality trends, create AQI forecasting models, and build environmental monitoring applications. ✅ Health & Epidemiology: Analyze correlations between air pollution and respiratory diseases, cardiovascular conditions, and general health. ✅ Climate & Environmental Research: Study pollution patterns, seasonal variations, and their relation to climate change. ✅ Urban Planning & Policy Making: Help city planners implement better pollution control strategies.

Why This Dataset? 📌 10-year coverage (2015-2025) for long-term trend analysis. 📌 Global scope with diverse geographical representation. 📌 Multiple pollutants & weather data for comprehensive insights. 📌 Ready-to-use for ML models, EDA, and research.

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.

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

Dallas Fed Manufacturing Shipments Index in the United States increased to 15.10 points in November from 5.80 points in October of 2025. This dataset includes a chart with historical data for the United States Dallas Fed Manufacturing Shipments Index.

China Index: CSI 300 Index data was reported at 4,531.050 31Dec2004=1000 in 03 Dec 2025. This records a decrease from the previous number of 4,554.330 31Dec2004=1000 for 02 Dec 2025. China Index: CSI 300 Index data is updated daily, averaging 3,804.009 31Dec2004=1000 from Apr 2005 (Median) to 03 Dec 2025, with 5020 observations. The data reached an all-time high of 5,807.719 31Dec2004=1000 in 10 Feb 2021 and a record low of 2,086.970 31Dec2004=1000 in 20 Mar 2014. China Index: CSI 300 Index data remains active status in CEIC and is reported by China Securities Index Co., Ltd.. The data is categorized under High Frequency Database’s Financial and Futures Market – Table CN.ZA: China Securities Index : Daily. [COVID-19-IMPACT]