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/

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.

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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.

Dataset

This dataset was created by Sergio Barreto97

Contents

Egypt EG: Stocks Traded: Total Value data was reported at 14.429 USD bn in 2017. This records an increase from the previous number of 10.080 USD bn for 2016. Egypt EG: Stocks Traded: Total Value data is updated yearly, averaging 21.767 USD bn from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 95.827 USD bn in 2008 and a record low of 10.080 USD bn in 2016. Egypt EG: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.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.

The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on scientific libraries in Bavaria 2006: Total digital holdings (number) (excluding electronic periodicals and newspapers), Total digital holdings - access, Total digital holdings - disposal, Total digital holdings - expenditure, Digital holdings (number), including: Stock databases, digital stocks, including: Access Databases - Access, Digital Stocks, including: Departure Databases - Departure, Digital Stocks, including: Expenditure on databases - Expenditure

Time Series Databases Software Market Outlook

The global time series databases software market is experiencing significant expansion, with market size estimated at approximately USD 1.5 billion in 2023 and projected to reach USD 4.2 billion by 2032, registering a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is driven by the increasing need for real-time analytics and the management of time-stamped data across various industry verticals. The proliferation of IoT devices and the growing importance of time-stamped data in decision-making processes are key factors contributing to this upward trajectory. As businesses seek to leverage these capabilities, the demand for efficient time series databases continues to rise.

One of the major growth factors driving the time series databases software market is the burgeoning IoT ecosystem. With millions of devices generating vast amounts of data every second, there is an unprecedented demand for systems that can efficiently process, store, and analyze time-stamped data. IoT applications, such as smart cities, connected vehicles, and industrial automation, rely heavily on real-time data insights to optimize operations and improve outcomes. Consequently, organizations are investing in advanced time series databases to harness the potential of IoT-driven data streams effectively. This trend is expected to accelerate as IoT adoption continues to grow across various sectors.

Another pivotal growth factor is the increasing emphasis on predictive analytics and machine learning across industries. Time series databases play a crucial role in these areas by enabling businesses to analyze historical data patterns and predict future trends. In sectors like finance, healthcare, and energy, the ability to forecast future events accurately can lead to improved decision-making and strategic planning. For instance, financial institutions utilize time series databases for stock market analysis, while healthcare providers use them for patient monitoring and prognosis. This growing reliance on predictive analytics is expected to fuel the demand for time series database solutions in the coming years.

The need for high-performance and scalable data architectures is also contributing to market growth. Traditional relational databases are often ill-equipped to handle the unique challenges posed by time-stamped data, such as high write and query loads and the need for efficient compression and data retention strategies. Time series databases are specifically designed to address these challenges, offering features such as efficient storage, fast retrieval, and seamless integration with analytics tools. As organizations grapple with increasingly large datasets, the adoption of time series databases is anticipated to rise, driven by the demand for scalable and cost-effective solutions.

Regionally, North America holds a significant share of the time series databases software market, driven by the presence of numerous tech-savvy industries and a strong focus on digital transformation. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid industrialization, the expansion of smart city initiatives, and increasing investments in IoT infrastructure. Europe also presents substantial growth prospects due to the growing adoption of advanced analytics solutions across various sectors. Meanwhile, Latin America and the Middle East & Africa are gradually embracing these technologies, albeit at a slower pace, as infrastructure and digital initiatives continue to develop. Each region's growth trajectory is influenced by local economic conditions, technology adoption rates, and regulatory frameworks.

Deployment Type Analysis

The analysis of deployment types in the time series databases software market reveals a dynamic landscape shaped by varying organizational needs and technological preferences. On-premises deployment remains a viable option for many businesses, particularly those in regulated industries where data security and control are paramount. Organizations in sectors such as finance and healthcare often prefer on-premises solutions to maintain stringent control over their data environments. These deployments offer the advantage of complete data custody and the flexibility to tailor configurations to specific organizational requirements. However, these benefits come with the trade-offs of higher upfront costs and the need for in-house technical expertise to manage and maintain the infrastructure effectively.

On the other hand, the cloud-based deployment model is witnessing

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.

Custommade Historical Financial Data For 230M Companies Worldwide: - Data from 2017, 2018, 2019, 2020 & 2021 - Includes turnover, employee size. - Custommade based on geographical location, turnover range, employee range and industry type - Standardized database for all countries

Make data work for you. With unbeatable data, skilled data experts and smart technology, we help businesses to unlock the power of international data.

Comprehensive database of European stock exchanges and trading venues

Carbon stock data that covers: five carbon pools: above- and belowground biomass, woody debris and litter, understorey and soil. As a Meta Database, it also provides general information related to the site, such as geographic location, land cover or forest type, and climate and soil. The data is harvested from CIFOR's Forest carbon database system (ForestCDB), is part of the effort to support initiatives such as greenhouse gas inventories, the development of forest reference emission level, monitoring, reporting and verification. ForestCDB invites its visitors, especially those who maintain forest inventory data, manage permanent sample plots or conduct research on carbon stocks, to participate in this initiative.

Series Is Presented Here As Three Variables--(1)--Original Data, 1855-1937 (2)--Original Data, 1918-1957 (3)--Original Data, 1949-1964. The 1855 And 1856 Figures Are An Unweighted Arithmetic Index Spliced On To The Weighted Index In January, 1857. They Are Not Considered Reliable By NBER. The Unit For This Variable Is Dollars Per Share. The Index Is A Chain Index Made Up Of 13 Month Segments Chained To The Segment 01/1926-01/1927. The Index Is Weighted According To The Number Of Shares Outstanding Each Year. Source: F.R. Macaulay, "Some Theoretical Problems Suggested By The Movements Of Interest Rates, Bond Yields, And Stock Prices In The United States Since 1856, " (NBER 1938), Pp.A142-A161.

This NBER data series m11005 appears on the NBER website in Chapter 11 at http://www.nber.org/databases/macrohistory/contents/chapter11.html.

NBER Indicator: m11005

The Stock Exchange Was Closed August-November, 1914. There Are No Data For August And September And Only Unofficial Data For October And November. The December, 1919 Entry Was Misprinted As 224.6 In The June, 1923 Supplement To The Review Of Economic Statistics, P.169. Here It Is Given As 229.6. The Total For 1923 Is Of Data For 5 Months. Source: Frickey, Review Of Economic Statistics, August, 1921, Pp.275-277; June, 1923 Supplement (Statistical Record) P.169

This NBER data series m11007 appears on the NBER website in Chapter 11 at http://www.nber.org/databases/macrohistory/contents/chapter11.html.

NBER Indicator: m11007

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on scientific libraries in Bavaria 2003: Total digital holdings (number) (excluding electronic periodicals and newspapers), Total digital holdings - access, Total digital holdings - disposal, Total digital holdings - expenditure, Digital holdings (number), including: Stock databases, digital stocks, including: Access Databases - Access, Digital Stocks, including: Departure Databases - Departure, Digital Stocks, including: Expenditure on databases - Expenditure

This data archive describes region definitions for the RAM Legacy Stock Assessment Database. Within the RAM Legacy database, stock assessments are associated with named areas. We approximate coordinates and bounding boxes for each of these areas, using country EEZs and fishing area shapefiles when appropriate. In addition, we develop a simple language to encode the GIS shapes of the areas, along with an interpreter to translate these codes into polygons. The syntax supports using political entities, shapefile regions, circles and rectangles, clipped versions of these, and combinations of these.

The archive contains the following contents:

• syntax.pdf: This document describes the geocoding syntax, and lists all of the geocoding descriptions for the assessment regions.

• results: This folder contains a shapefile of assessment regions (ram.shp) and a summary file of each region's centroid and size.

• sources: This folder contains shapefiles for FAO regions and New Zealand fishing regions, used by the syntax system, and latlon.csv which contains the region descriptions for each assessment region.

• code: load_areas.R contains functions that interpret the geocoding syntax and genshape.R generates the ram.shp shapefile.

Comprehensive dataset of 23,820 Stock brokers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This data release presents the Yale stocks and flows database (YSTAFDB). Its data describe the use of 102 materials from the early 1800s to circa 2013 through anthropogenic cycles, their recycling and criticality properties, and on spatial scales ranging from suburbs to global. This data collection was previously scattered across multiple non-uniformly formatted files such as journal papers, reports, and unpublished spreadsheets. These data have been synthesized into YSTAFDB, which is presented as individual comma-separated text files and also in MySQL and PostgreSQL database formats. Consolidation of these data into a single database can increase their accessibility and reusability, which is relevant to diverse stakeholders ranging from researchers in sustainability science to government employees involved in national emergency planning.

Series Is Presented Here As Two Variables--(1)--Original Data, 1897-1916 (2)--Original Data, 1914-1958 20 Stocks Are Used Through September, 1928 And 30 Stocks Thereafter. A Detailed Description Of Methods Of Constucting Averages Is Given In "Basis Of Calculation Of Dow-Jones Average" Available From The Wall Street Journal. For A More Detailed Description Of The Series, See Business Cycle Indicators, Vol. Ii, Moore, NBER. This Index Is Based On Daily Closing Prices On The New York Stock Exchange. Through 1948, Averages Of Highest And Lowest Indexes For The Month Are Used. For 1949-1968, Averages Of Daily Closing Indexes Are Used. Source: Data Were Compiled By Dow Jones And Company From Quotations In The Wall Street Journal. Through June, 1952, Data Are From The Dow-Jones Averages, 13Th Edition, 1948, And Supplementary Averages (Barron'S Publishing Company). Thereafter, Through 1968, Data Are From Barron'S National Business And Financial Weekly.

This NBER data series m11009b appears on the NBER website in Chapter 11 at http://www.nber.org/databases/macrohistory/contents/chapter11.html.

NBER Indicator: m11009b