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.

This dataset contains the full historical record of the S&P 500 index (^GSPC), downloaded via the Yahoo Finance API using the yfinance Python library.

The dataset includes: - Date: Trading date - Open, High, Low, Close: Daily price levels - Volume: Daily trading volume

Period covered: Dec 30, 1927 – Aug 31, 2025 Frequency: Daily

⚠️ Disclaimer: This dataset is provided for educational and research purposes only. Redistribution or commercial use may be subject to Yahoo Finance’s Terms of Service

License

Data sourced from Yahoo Finance. Provided for educational and research purposes only. Redistribution may be restricted.

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.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

This dataset contains daily historical price data for five different stock portfolios alongside major market indices such as NDX, SPX, SML. It is intended for financial analysis, portfolio comparison, risk assessment, and algorithmic trading research. The dataset provides a clean and structured format suitable for time series analysis and machine learning applications.

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is google trends of stock (Dow, S&P500 index, Nasdaq index to update later) from pytrends (It is not official). Contains value of trend's result normalized as date of about 1 year (2020-06-14, 2021-06-06).

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want to compare stock's recent price, you should check this data set and refer to the Notebook.

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In Trend_sp500.json It is presented that trend of google to be normalized by index of S&P500

• In Trend_dow.json. It is presented that trend of google to be normalized by index of Dow

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

It is funning model comparing trend of google if it has correlation or not.

This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code,Please see another data sets like S&P500 price and financial statements, Dow price and financial statements

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.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Comparison of the results of different parameters evaluation indexes.

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

Dataset

This dataset was created by Confusion

Contents

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.

Systemic immune inflammation index, systemic inflammatory response index and pan-immune inflammation value in predicting nausea and vomiting in pregnancy and the need for hospitalization Abstract Objective To investigate the role of the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI) and pan-immune inflammation value (PIV) in predicting nausea and vomiting in pregnancy (NVP). Study Design Women diagnosed and treated for NVP at a large tertiary hospital between 2016 and 2021 were retrospectively analyzed. After applying the inclusion criteria, a total of 278 eligible patients with NVP and 278 gestational age-matched healthy pregnant women were included. Patients with NVP were divided into mild (n=58), moderate (n=140) and severe NVP (n=80). Patients with moderate and/or severe NVP who were at high risk for hospitalization were pooled and assigned to an inpatient treatment group. The data from the first trimester of the groups were then compared. Results SII and PIV were significantly higher in the NVP group than in the control group, while SII, SIRI and PIV were significantly higher in the inpatient treatment group than in the mild NVP group. The comparison of overall performance in predicting the development of NVP showed that SII was better than PIV (p1207x103/µL (47.48% sensitivity, 82.01% specificity) had the highest discriminatory power for predicting NVP. Conclusion Our results suggest an association between high SII and PIV and an increased risk of future NVP. These markers can be used as a first-trimester screening test to improve treatment planning of pregnancies at high risk of HG.

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.

This is the data used for the development of the Index Index model.

Comparison of mean Cq values for index and reference assays.

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).

Corporate Services Price Index (CSPI) has been discontinued. Data is now available as the Services Producer Price Index (SPPI), a quarterly survey of prices charged for a range of services provided by businesses to other businesses and government. Source agency: Office for National Statistics Designation: Experimental Official Statistics Language: English Alternative title: CSPI

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.