United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value 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: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Molport In-Stock Database

The Molport In-Stock Database contains SMILES strings and Molport IDs for all 5.9 million in-stock molecules, covering both screening compounds and building blocks that are currently available for purchase.

  Contents

SMILES – The molecular structure in SMILES notation. SMILES_CANONICAL – Canonicalized SMILES representation. MOLPORTID – Unique Molport identifier for each compound.

  Scope

This dataset includes:

All screening compounds… See the full description on the dataset page: https://huggingface.co/datasets/molport/In-stock-Database.

Dataset

This dataset was created by Joseph Armstrong

Released under Database: Open Database, Contents: © Original Authors

Contents

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.

New York Stock Exchange: Index: S&P Health Care Services Select Industry Index data was reported at 16,242.170 NA in Jan 2026. This records an increase from the previous number of 16,233.750 NA for Dec 2025. New York Stock Exchange: Index: S&P Health Care Services Select Industry Index data is updated monthly, averaging 9,662.040 NA from Aug 2013 (Median) to Jan 2026, with 150 observations. The data reached an all-time high of 17,438.350 NA in Jun 2021 and a record low of 5,989.190 NA in Aug 2013. New York Stock Exchange: Index: S&P Health Care Services Select Industry 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: S&P: Monthly.

Dataset

This dataset was created by Sergio Barreto97

Contents

The Center for Research in Security Prices (CRSP) stock databases provide time-series and event data on individual stocks, augmented with market time-series. Daily and monthly time-series variables include returns, closing, low bid and high ask prices, and trading volume. Event data includes distributions, shares outstanding, names, etc.

Dataset is an external database available here for Cornell affiliates: https://johnson.library.cornell.edu/database/wharton-research-data-services-wrds/

New York Stock Exchange: Index: S&P 500 Real Estate Index data was reported at 262.030 NA in Jan 2026. This records an increase from the previous number of 255.030 NA for Dec 2025. New York Stock Exchange: Index: S&P 500 Real Estate Index data is updated monthly, averaging 212.980 NA from Jan 2012 (Median) to Jan 2026, with 169 observations. The data reached an all-time high of 324.750 NA in Dec 2021 and a record low of 135.740 NA in Feb 2012. New York Stock Exchange: Index: S&P 500 Real Estate 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: S&P: Monthly.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

It is not so often that one can find fundamental data of companies on which it would be possible to accurately assess the value of a company.

So I decided to use yahoo_fin api to collect some fundamentals of 48 companies from the S&P 500 index.

The content of indicators in each table: - total assets. - cash. - stockholder equity. - profit. - revenue. - return on equity, return on assets, profit margin. - trailing P/E, P/S, P/B, PEG, forward P/E.

In addition, the dataset has prices for all stocks for four years.

The description for this record is not currently available.

This database provides a centralized, structured summary of key information related to Netflix Inc. (NFLX) in the context of stock market tracking and financial analysis.

The goal is to offer quick, reliable access to information for the user that make you have the power of make summaries of content that helps users understand Netflix’s performance, strategy, and value in the stock market. This is especially useful for traders, analysts, investors, and financial platforms integrating Netflix data into dashboards or feeds.

Usage

🙋 Improving data: Use this information to use the data engineering making more interesting and profitable data for new projects

📊 Financial Analysis: Analyze trends in Netflix stock performance over time.

🤖 Machine Learning Models: Train models for price prediction, volatility estimation, or portfolio simulation.

📈 Time Series Forecasting: Apply statistical models (ARIMA, LSTM, Prophet) to forecast future stock movements.

🧮 Correlation Studies: Compare META’s stock with other tech giants like , or economic indicators (e.g. inflation, rates).

📰 Event Impact Analysis: Study how product launches, earnings calls, or regulatory news affect META’s stock.

💼 Investment Strategy Backtesting: Test strategies like momentum investing, mean reversion, or moving average crossovers.

🌐 Market Sentiment Studies: Combine with social media/news sentiment to explore relationships with stock movement.

Column Description

• Date: The trading date of the stock
• Open: Trading stock price of the day
• High: Higher trading stock of the day
• Low: Lower trading stock of the day
  • Close: The stock's closing price on that day.
  • Volume: The amount of stocks traded on that day.
  • Dividends: Dividends given by the company (In this case 0)
  • Stock Splits
  • Transaction_Value: The average amount of money traded on that day (Thats the average price of the stock multiplied by the volume of the stocks traded)

Extra Details of Dataset

• Source: Yahoo Finance Ticker
• Ticker : NFLX Data
• Format: CSV Date
• Range: 1993 - July 2025
• File Size: Varies based on data range

The RAM Legacy Stock Assessment Database is a compilation of stock assessment results for commercially exploited marine populations from around the world. The RAM Legacy Stock Assessment Database is grateful to the many stock assessment scientists whose work this database is based upon and the many collaborators who recorded the assessment model results for inclusion in the RAM Legacy Stock Assessment Database. Since 2011 the RAM Legacy Data base has been hosted and managed at the University of Washington with financial assistance from a consortium of Seattle-based seafood companies and organizations, and from the Walton Family Foundation. Initial development of the database from 2006-2010 was supported by the Census of Marine Life, Canadian Foundation for Innovation, NCEAS, NSERC, the Smith Conservation Research Fellowship, New Jersey Sea Grant, and the National Science Foundation.

Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.

This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (MISO2) (Wiedenhofer et al. 2024). MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository.

The MAT_STOCKS database version 1.0 provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:

• Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)
• Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)
• Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)
• 177 countries
• 1900 to 2016

All units in kilotons. Paramter names are in accordance with the system definition given in the publication.

Additionally, this repository includes all data presented in the figures of the related journal article.

Further information

This dataset complements the following scientific article:

Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, https://doi.org/10.1111/jiec.13575

The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer dominik.wiedenhofer(a)boku.ac.at and visit our website to learn more about our project: MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Funding

This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.

We complied the China soil bulk density and organic carbon stock database. This database inlcudes 18,945 and 15,389 soil samples with bulk density in fine fraction (Bdfine) and soil organic cabron stock (SOCS) for the EU and UK using the best traditional pedotransfer function (T-PTF-4) and machine leanring based PTFs (Local-RFFRFS). It also contains the POINTID linked to LUCAS Soil 2018, coarse fragements in volume (coarse_vol) and coordinates (GPS_LAT, GPS_LONG).

This dataset is asscoated to the "A soil organic carbon density database (2010-2024) using ensemble modelling-based pedotransfer functions in China" by Chen et al. (2025).

Manuscript citation: Chen, Z., Chen, L., Lu, R., Lou, Z., Zhou, F., Jin, Y., Xue, J., Guo, H., Wang, Z., Wang, Y., Liu, F., Song, X., Zhang, G., Su, Y., Ye, S., Shi, Z., Chen, S., 2025. A soil organic carbon density database (2010-2024) using ensemble modelling-based pedotransfer functions in China. Earth System Science Data, 16, 2367–2383.

When using the data, please cite repositories as well as the original manuscript.

For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data was reported at 843.650 NA in Jan 2026. This records an increase from the previous number of 786.750 NA for Dec 2025. New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data is updated monthly, averaging 614.550 NA from Aug 2013 (Median) to Jan 2026, with 150 observations. The data reached an all-time high of 843.650 NA in Jan 2026 and a record low of 395.070 NA in Aug 2013. New York Stock Exchange: Index: S&P Consumer Staples Select Sector 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: S&P: Monthly.

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

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Stonks by unknown memer.

The Maize Genetics Cooperation Stock Center is operated by USDA/ARS, located at the University of Illinois, Urbana/Champaign, and integrated with the National Plant Germplasm System (NPGS). The center serves the maize research community by collecting, maintaining and distributing seeds of maize genetic stocks, and providing information about maize stocks and the mutations they carry through the Maize Genetics and Genomics Database (MaizeGDB). Users can browse to obtain detailed information about the following stocks:

Chromosome 1 Markers Chromosome 2 Markers Chromosome 3 Markers Chromosome 4 Markers Chromosome 5 Markers Chromosome 6 Markers Chromosome 7 Markers Chromosome 8 Markers Chromosome 9 Markers Chromosome 10 Markers Unplaced Genes Multiple Genes Rare Isozyme B-Chromosome Alien Addition Trisomic Tetraploid Cytoplasmic-Sterile / Restorer Cytoplasmic Trait Toolkit B-A Translocations (Basic Set) B-A Translocations (Others) Inversion Reciprocal Translocations (wx1 and Wx1 marked)

Stock records include information on availability, annotations, related records (genotypic variations, phenotypes), GRIN (Germplasm Resources Information Network) information, and offsite resources. Resources in this dataset:Resource Title: Maize Genetics Cooperation Stock Center Catalog of Stocks web link. File Name: Web Page, url: https://www.maizegdb.org/stock_catalog A tool in the MaizeGDB system.

Descripción de la base de datos

Para este dataset se cuenta con 5 tablas. Los productos son 20 en total, cada uno tiene un stock máximo que se elige de forma aleatoria entre 50 y 120 unidades, un precio inicial que se elije entre 1500 y 3000 y un promedio de compras de .2 a .7.

El precio de los productos puede variar con 10% de incremento, decremento o 0% con la misma probabilidad.

Cada día la tienda puede hacer entre 3 y 8 facturas, y por cada factura un cliente puede comprar entre 1 y 5 productos, y por producto una cantidad que sigue a distribución binomial

Limites de las fechas: 2015/01/01 - 2024/12/31

Tenga en cuenta que los datos se generaron con estas simples reglas, por lo cual es normal encontrar datos que se comporten de forma extraña, este dataset está pensado para practicar.

Diccionario de datos

PRODUCTO

PRECIO

STOCK

FACTURA

FACT_PROD