The Centers for Disease Control Social Vulnerability Index shows which communities are especially at risk during public health emergencies because of factors like socioeconomic status, household composition, racial composition of neighborhoods, or housing type and transportation. The CDC SVI uses 15 U.S. census variables to identify communities that may need support before, during, or after disasters. Learn more here. The condition is the overall ranking of four social theme rankings where lower values indicate high vulnerability and high values indicate low vulnerability. Quintiles for this condition were determined for all the Census tracts in King County. Quintile 1 is the most vulnerable residents, Quintile 5 is the least vulnerable residents. Data is released every 2 years following the American Community Survey release in December of the year following the Survey. The most recent data for 2018 was downloaded from the ATSDR website.

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.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

NCHS has linked data from various surveys with death certificate records from the National Death Index (NDI). Linkage of the NCHS survey participant data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality. The Linked Mortality Files (LMF) have been updated with mortality follow-up data through December 31, 2019.

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 Elemental Data Index provides access to the holdings of NIST Physical Measurement Laboratory (PML) online data organized by element. It is intended to simplify the process of retrieving online scientific data for a specific element from various online databases, including atomic spectroscopy, atomic data, x-ray absorption, and nuclear data. For some of the databases, the data are immediately retrieved; for others, the retrieval form is provided with the element entered in the form, but additional options must be selected in order to retrieve the data. Each of the databases can be individually accessed from the PML's Physical Reference Data page (http://pml.nist.gov/data).

Discover published data which is local in nature. A local search will return results which include the statewide dataset, which can then be searched and/or filtered to view a specific locality. For numerous statewide datasets, it provides quick access to local information across a broad range of categories from health to transportation, from recreation to economic development; Find local farmer’s markets, child care regulated facilities, solar installations, food service establishment inspections, and much more. Datasets may be searched on one or more local attributes (e.g., county, city), depending upon the granularity of the data. See the overview document http://on.ny.gov/1SB66oL in the “About” section of the source dataset for ways to search specific localities within Statewide datasets.

United States Index: Value Line: Arithmetic data was reported at 6,053.860 21May1985=100 in Nov 2018. This records an increase from the previous number of 5,958.610 21May1985=100 for Oct 2018. United States Index: Value Line: Arithmetic data is updated monthly, averaging 1,326.970 21May1985=100 from Jan 1989 (Median) to Nov 2018, with 359 observations. The data reached an all-time high of 6,604.520 21May1985=100 in Aug 2018 and a record low of 216.890 21May1985=100 in Oct 1990. United States Index: Value Line: Arithmetic data remains active status in CEIC and is reported by Value Line. The data is categorized under Global Database’s United States – Table US.Z019: Valueline: Index.

Index Data Management Market Outlook

As per our latest research, the global index data management market size reached USD 5.4 billion in 2024, reflecting robust demand for advanced data management solutions across diverse industries. The market is expected to maintain a strong growth trajectory, registering a CAGR of 13.2% from 2025 to 2033. By the end of 2033, the global index data management market is forecasted to surpass USD 16.5 billion. This impressive growth is primarily driven by the surging volume of enterprise data, the increasing need for regulatory compliance, and the rapid digital transformation initiatives being undertaken by organizations worldwide.

The exponential growth in data generation, particularly from digital platforms, IoT devices, and business applications, is a significant growth driver for the index data management market. Organizations are increasingly recognizing the value of structured and unstructured data as strategic assets, necessitating robust solutions for efficient data indexing, discovery, and retrieval. The proliferation of big data analytics, artificial intelligence, and machine learning applications further amplifies the demand for advanced index data management platforms that can seamlessly handle complex data sets, enhance data accessibility, and support real-time decision-making processes. As enterprises strive to leverage data for competitive advantage, the adoption of comprehensive index data management solutions is set to accelerate across all major industry verticals.

Another major factor fueling the expansion of the index data management market is the growing emphasis on data governance and regulatory compliance. With the implementation of stringent data protection regulations such as GDPR, CCPA, and other regional mandates, organizations are under increasing pressure to ensure data accuracy, traceability, and security. Index data management solutions play a crucial role in facilitating compliance by providing centralized data catalogs, metadata management, and robust audit trails. These capabilities not only mitigate compliance risks but also enhance organizational transparency and trust. As regulatory frameworks continue to evolve, the necessity for scalable and adaptable index data management systems is expected to intensify, further propelling market growth.

The rapid adoption of cloud computing and hybrid IT infrastructures is also transforming the index data management landscape. Enterprises are increasingly migrating their workloads to the cloud to achieve greater scalability, flexibility, and cost efficiency. This shift necessitates sophisticated index data management solutions that can operate seamlessly across on-premises, cloud, and hybrid environments. Cloud-based index data management platforms offer enhanced integration capabilities, real-time data synchronization, and improved collaboration among distributed teams. As digital transformation initiatives gain momentum, particularly in sectors such as BFSI, healthcare, and retail, the demand for cloud-native index data management solutions is poised to surge, shaping the future trajectory of the market.

From a regional perspective, North America currently dominates the index data management market, accounting for the largest share owing to the presence of leading technology providers, early adoption of advanced data management practices, and a highly digitized business ecosystem. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid digitalization, increasing investments in IT infrastructure, and the expanding presence of global enterprises. Europe also represents a significant market, supported by stringent data privacy regulations and a strong focus on data-driven innovation. As organizations across all regions prioritize data-driven strategies, the global index data management market is expected to witness sustained growth through 2033.

Component Analysis

The index data management market is segmented by component into software and services, each playing a pivotal role in driving market growth and adoption. The software segment encompasses a wide array of solutions designed for data cataloging, metadata management, data quality, and master data management. These software solutions are increasingly being integrated with artificial intelligence and machine learning capabilities, enabling organizations to automate data discovery, classification, a

Leaf Area Index data from the KBS GLBRC...

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.

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

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.

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UPDATED EVERY WEEK Last Update - 26th July 2025

Disclaimer!!! Data uploaded here are collected from the internet and some google drive. The sole purposes of uploading these data are to provide this Kaggle community with a good source of data for analysis and research. I don't own these datasets and am also not responsible for them legally by any means. I am not charging anything (either money or any favor) for this dataset. RESEARCH PURPOSE ONLY

Context

• The NIFTY 50 is a well-diversified 50 stock index and it represents 13 important sectors of the economy.
• It is used for a variety of purposes such as benchmarking fund portfolios, index-based derivatives, and index funds.
• NIFTY 50 is owned and managed by NSE Indices Limited.
• The NIFTY 50 index has shaped up to be the largest single financial product in India.

This data contains all the indices of NSE. NIFTY 50, NIFTY BANK, NIFTY 100, NIFTY COMMODITIES, NIFTY CONSUMPTION, NIFTY FIN SERVICE, NIFTY IT, NIFTY INFRA, NIFTY ENERGY, NIFTY FMCG, NIFTY AUTO, NIFTY 200, NIFTY ALPHA 50, NIFTY 500, NIFTY CPSE, NIFTY GS COMPSITE, NIFTY HEALTHCARE, NIFTY CONSR DURBL, NIFTY LARGEMID250, NIFTY INDIA MFG, NIFTY IND DIGITAL, INDIA VIX

File Information and Column Descriptions.

Nifty 50 index data with 1 minute data. The dataset contains OHLC (Open, High, Low, and Close) prices from Jan 2015 to Aug 2024. - This dataset can be used for time series analysis, regression problems, and time series forecasting both for one step and multi-step ahead in the future. - Options data can be integrated with this minute data, to get more insight about this data. - Different backtesting strategies can be built on this data.

File Information

• This dataset contains 6 files, each file contains nifty 50 data with different intervals.
• Different intervals are - 1 min, 3 min, 5 min, 15 min, and 1 hour, Daily data from intervals of 2015 Jan to 2024 August.

Column Descriptors

• Each file contains OHLC (Open, High, Low, and Close) prices and Data time information. Since these are Nifty 50 index data, so volume is not present.

Inspiration

Time series forecasting - Predict stock price

• Predict future stock price one step ahead and multi-step ahead in time.
• Use different time series forecasting techniques for forecasting the future stock price. ### Machine learning and Deep learning techniques
• Possible ML and DL models include Neural networks, RNNs, LSTMs, Transformers, Attention networks, etc.
• Different error functions can be considered like RMSE, MAE, RMSEP etc. ### Feature engineering
• Different augmented features can be created and that can be used for forecasting.
• Correlation analysis, Feature importance to justify the important features.

Case-Shiller Index of US residential house prices. Data comes from S&P Case-Shiller data and includes both the national index and the indices for 20 metropolitan regions. The indices are created us...

The global Ocean Health Index measures the state of the world’s oceans.The global OHI score for the 2024 assessment was 69, which was quite a bit lower than last year’s score of 73. This was due to COVID-related declines in tourism and recreation [the 2024 scores reflect 2021 data]. You can explore this and other goals using the interactive map which shows how different countries and goals contribute to the global score, as well as how the score has changed since 2012. Click on colored regions (i.e. EEZs) to see short country summaries.

This data contains Index match, index match Advance

Graph and download economic data for Producer Price Index by Industry: Data Processing, Hosting and Related Services (PCU518210518210) from Dec 2000 to Aug 2025 about information technology, processed, services, PPI, industry, inflation, price index, indexes, price, and USA.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.