This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. To identify the owner of a business, you will need the account number or legal name, which may be obtained from theBusiness Licenses dataset: https://data.cityofchicago.org/dataset/Business-Licenses/r5kz-chrr. Data Owner: Business Affairs & Consumer Protection. Time Period: 2002 to present. Frequency: Data is updated daily.

This dataset provides a structured and machine-readable register of all companies recorded by the Companies Registration Office (CRO) in Ireland. It includes a daily snapshot of company records, covering both currently registered companies and historical records of dissolved or closed entities. The dataset aligns with the European Union’s Open Data Directive (Directive (EU) 2019/1024) and the Implementing Regulation (EU) 2023/138, which designates company and company ownership data as a high-value dataset. Updated daily, it ensures timely access to corporate information and is available for bulk download and API access under the Creative Commons Attribution 4.0 (CC BY 4.0) licence, allowing unrestricted reuse with appropriate attribution. By increasing transparency, accountability, and economic innovation, this dataset supports public sector initiatives, research, and digital services development. .hidden { display: none }

The 20,000+ registered companies with a registered address in Glasgow. The information is extracted from Companies House. It includes the company name, number, category (private limited, partnership), registered address, postcode, industry (SIC code), status (ex: active or liquidation), incorporation date... It is likely that some companies may just lie off Glasgow City Council's boundary. If you find a problem in the data, you can check the source either in the full UK list or by looking up a company or let us know. The data dictionary supplied by Companies House can be viewed here. There is also a data dictionary with field names and meanings contained in the resources. This dataset does not imply: - a partnership with Companies House - an endorsement by Companies House - a product approval by Companies House Licence: None glasgow-post-codes-py.txt - https://dataservices.open.glasgow.gov.uk/Download/Organisation/cc57ac4b-12d5-43b1-ad25-434638eec18c/Dataset/3093e34f-6dcb-4980-840b-965421c1b091/File/c2634107-bd43-4537-adb8-9046aeed844e/Version/c8fde78e-5396-4293-ac35-6f6c96a5d642

Overview

This dataset contains a collection of over 2,000 company documents, categorized into four main types: invoices, inventory reports, purchase orders, and shipping orders. Each document is provided in PDF format, accompanied by a CSV file that includes the text extracted from these documents, their respective labels, and the word count of each document. This dataset is ideal for various natural language processing (NLP) tasks, including text classification, information extraction, and document clustering.

Dataset Content

PDF Documents: The dataset includes 2,677 PDF files, each representing a unique company document. These documents are derived from the Northwind dataset, which is commonly used for demonstrating database functionalities.

The document types are:

• Invoices: Detailed records of transactions between a buyer and a seller.
• Inventory Reports: Records of inventory levels, including items in stock and units sold.
• Purchase Orders: Requests made by a buyer to a seller to purchase products or services.
• Shipping Orders: Instructions for the delivery of goods to specified recipients.

Example Entries

Here are a few example entries from the CSV file:

Shipping Order:

• Order ID: 10718
• Shipping Details: "Ship Name: Königlich Essen, Ship Address: Maubelstr. 90, Ship City: ..."
• Word Count: 120

Invoice:

• Order ID: 10707
• Customer Details: "Customer ID: Arout, Order Date: 2017-10-16, Contact Name: Th..."
• Word Count: 66

Purchase Order:

• Order ID: 10892
• Order Details: "Order Date: 2018-02-17, Customer Name: Catherine Dewey, Products: Product ..."
• Word Count: 26

Applications

This dataset can be used for:

• Text Classification: Train models to classify documents into their respective categories.
• Information Extraction: Extract specific fields and details from the documents.
• Document Clustering: Group similar documents together based on their content.
• OCR and Text Mining: Improve OCR (Optical Character Recognition) models and text mining techniques using real-world data.

Free monthly CSV files showing all newly incorporated, reinstated, and dissolved UK companies. Updated monthly with official Companies House data.

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

This dataset contains counts of active business registrations in Pennsylvania and is broken down by County. For a Distinct count and view of each business, please select the Filtered View here OR select the Storyboard here for additional information and help with filtering the dataset.
That View will provide a dataset with one row per business, and an opportunity to query and download the data. The Storyboard provides general information about the business registration process, offers a multiple selection filter option, along with a view of the data and an opportunity to download the data. It also provides additional links to help with filing a business and a link to the Department of State website. There can be multiple rows per business due to the Governor/Officer column identifying those individuals with those positions in the company.
ALL active business registrations in the data to include: business corporations, nonprofit corporations, limited liability companies, limited partnerships, limited liability partnerships, limited liability limited partnerships, business trusts, land banks, municipal and other authorities.
**Due to statutory limitations in removing businesses no longer in operation from our database, this data shows a larger number of active businesses than currently exist.
Note: Previously titled Active Business Registrations Year 1768 – Current by County

Graph and download economic data for Business Applications from Corporations: Information in the United States (BACBANAICS51SAUS) from Jul 2004 to Dec 2021 about business applications, information, business, corporate, and USA.

This dataset provides a structured and machine-readable collection of financial statements filed with the Companies Registration Office (CRO) in Ireland. It currently includes financial statements for the year 2022, with additional years to be added as they become available. The dataset aligns with the European Union’s Open Data Directive (Directive (EU) 2019/1024) and the Implementing Regulation (EU) 2023/138, which designates company and company ownership data as a high-value dataset. It is available for bulk download and API access under the Creative Commons Attribution 4.0 (CC BY 4.0) licence, allowing unrestricted reuse with appropriate attribution. By increasing transparency and enabling data-driven insights, this dataset supports public sector initiatives, financial analysis, and digital services development. The API endpoints can be accessed using these links - Query - https://opendata.cro.ie/api/3/action/datastore_search Query (via SQL) - https://opendata.cro.ie/api/3/action/datastore_search_sql

Listing of all (active and inactive) businesses registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly. NAICS Codes are from 2007 NAICS: https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

▲ INSEE modernises its API portal, building on a new architecture ▲:

The general conditions of use of the portal, as well as those of the APIs presented, remain unchanged. Under the URL of the new portal https://portail-api.insee.fr/, you will find INSEE’s dissemination APIs.

Access to the SIRENE API:

To access the SIRENE API, you must first create an account on the new portal and then subscribe to the API. Instructions for use here. The same account can subscribe to several APIs, following the same procedure.

!! Attention, we advise you to quickly integrate this new environment, by the end of the year, the Sirene API will be accessible only from this new portal. !!

To subscribe to our newsletter Sirene open data news, click here To consult our newsletters Sirene open data news, click here

Stock files

• On May 1st, a new monthly file stockDoublons is proposed, it consists of the list of siren and their duplicates with the date of last treatment always in CSV format.
• The final stock files in 3.11 format are published on 26 March 2024 instead of the previous 3.9 files.

Six compacted monthly stock files (ZIP format) are available: - the stock file of legal units (active and discontinued legal units in their current state in the directory) - the stock file of the historical values of the legal units - the inventory file of establishments (active and closed establishments in their current state in the directory) - the stock file of the historical values of the establishments - the stock file of the succession links of the establishments - the stock file of duplicate siren

Each compacted file (ZIP format) contains a data file in CSV format. Files uploaded from the 1st of the month are an image of the Sirene directory as of the last day of the previous month. A stock file of a given month replaces that of the previous month. Discontinued legal units and closed establishments are included, thus providing access to Sirene data since 1973.

Updates

Infra-monthly updates of these files, including daily updates, are possible: - using the SIRENE APIs available on the catalogue of the INSEE APIs. With the API, you have access to variables indicating, for both establishments and legal units, the date of the last processing carried out. These are the variables dateLastUniteLegalTreatment and dateLastEstablishmentTreatment. Since this date is different from the date of the same record in your stock file, you know that an update has been made. Documentation on Sirene API variables and services is available on the [Documentation] tab (https://porttail-api.insee.fr/catalog/api/2ba0e549-5587-3ef1-9082-99cd865de66f/doc?page=52d26f24-963b-4fc0-926f-24963b4fc021) of each API; - using "Build a list" on sirene.fr (select Update Date tab) to be able to download files consisting of daily updates. You can consult the Sirene letter open data news n°2.

Siren database containing personal data, INSEE draws your attention to the legal obligations arising therefrom: - The processing of these data falls in particular under the obligations of the General Data Protection Regulation (GDPR), of Law 78-17 of 6 January 1978 as amended, known as CNIL Law - Depending on your use of the dataset, it is therefore your responsibility to take into account the most recent distribution status of each natural person, which takes into account the objections made by some of them, to the consultation or use of their SIRENE data by third parties other than authorized administrations or bodies. - Legal units or establishments which have a distribution status coded ‘P’ (resp. statusDiffusionUniteLegale or statusDiffusionEtablissement) are subject to partial dissemination of data following a request for opposition. For an objection by a natural person, the identity of the entrepreneur (surname, first names, etc.), the address in the municipality and the geolocation will be masked (i.e. not disseminated by the SIRENE API). In case of opposition by legal representatives of a legal person, the address of the establishment in the municipality and its geolocation will be hidden. It is understood that data relating to legal representatives are not disseminated by INSEE as Open Data, even in the absence of opposition, in accordance with**Article R 123-232** of the French Commercial C

Businees License Dataset Information Sheet

Market Inside is a global leader in import-export data and analytics for the major industries and markets. We accelerate business progress by delivering essential intelligence that unlocks opportunities and fosters growth.

Our database contains: • 220+ Countries’ Global Trade Data • 2+ Billion Importer-Exporter Shipment Records • 60+ Million Import-Export Companies • 40+ Million Decision Maker Direct Phone Numbers • 50+ Million Decision Maker Direct Email Addresses

By using our dashboard, customers can access: • Bill of lading data by HS Code or product description. • Product specifications like brand, model, type, etc. • Company information like name, size, location and so on. • Companies’ business information such as market share, industry, etc. • Contacts data including name & profile of employees and phone numbers & email addresses of key decision makers. • Location data like origin country, destination and port of loading & unloading. • Our import export data can be used by investment firms and banks, private equity firms, hedge funding corporations, government agencies, insurance firms, corporations, exporters and importers, environmental studies agencies, academics, logistics, pharma, FMCG, Consulting, Law Firms and other private players to track, analyze, research and gain better insights of global trade of more than 50M+ Commodities.

For data authenticity, we collect raw data from the most genuine sources like Federal & State Government, Embassies, Customs & Taxes Departments, Ministries, Ports Authorities and filter information to remove duplicates and make every detail clean and error-free. Our database is updated frequently and customers can directly view and download shipment records on keywords of their choice in seconds anywhere and anytime

Title: Stock Prices of 500 Biggest Companies by Market Cap (Last 5 Years)

Description: This dataset comprises historical stock market data extracted from Yahoo Finance, spanning a period of five years. It includes daily records of stock performance metrics for the top 500 companies based on market capitalization.

Attributes: 1. Date: The date corresponding to the recorded stock market data. 2. Open: The opening price of the stock on a given date. 3. High: The highest price of the stock reached during the trading day. 4. Low: The lowest price of the stock observed during the trading day. 5. Close: The closing price of the stock on a specific date. 6. Volume: The volume of shares traded on the given date. 7. Dividends: Any dividend payments made by the company on that date (if applicable). 8. Stock Splits: Information regarding any stock splits occurring on that date. 9. Company: Ticker symbol or identifier representing the respective company.

Usefulness: - Investors and analysts can leverage this dataset to conduct various analyses such as trend analysis, volatility assessment, and predictive modeling. - Researchers can explore correlations between stock prices of different companies, sector-wise performance, and market trends over the specified duration. - Machine learning enthusiasts can employ this dataset for developing predictive models for stock price forecasting or anomaly detection.

Note: Prior to using this dataset, it's recommended to perform data cleaning, handling missing values, and verifying the consistency of data across companies and time periods.

License: The dataset is sourced from Yahoo Finance and is provided for analytical purposes. Refer to Yahoo Finance's terms of use for further details on data usage and licensing.

Dataset from Accounting and Corporate Regulatory Authority. For more information, visit https://data.gov.sg/datasets/d_67e99e6eabc4aad9b5d48663b579746a/view

Graph and download economic data for Business Formations Within Four Quarters: Information in the United States (BFBF4QNAICS51SAUS) from Jul 2004 to Dec 2021 about business formations, information, business, and USA.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company DOWNLOAD.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company Digital-Download-Inc.

Explore the largest pre-crawled news articles dataset from CNBC, a leading global news source for business, finance and current affairs. This comprehensive news dataset includes thousands of articles covering a wide range of topics: financial markets, economic trends, technology, politics, health, and more. Each entry in this dataset provides detailed information, including headlines, publish dates, authors, article content and categories — offering valuable insights for researchers, data analysts and media professionals.

Key Features

• Extensive Coverage: Thousands of news articles from CNBC, spanning business, finance, technology and global news.
  
  
• Detailed Metadata: Each news record includes headline, publication date, author, content and category — enabling in-depth research.
  
  
• Ready for Analysis: Ideal for data scientists, content strategists and media researchers building trend-analysis, media-monitoring or news-analytics models.
  
  
• Immediate Use: Data is delivered in CSV format, instantly downloadable, ready for machine-learning, AI modelling or reporting pipelines.
  

Who benefits?

• Academic researchers analysing media coverage or news sentiment.
  
  
• Data scientists building news analytics dashboards or training NLP models.
  
  
• Content teams / SEO specialists sourcing datasets to support articles about media trends or data-driven stories.
  
  
• Global marketers investigating business & financial news trends across the US/UK/EU markets.
  

Why choose this dataset?

• Ready to use: no manual scraping required.
  
  
• High-quality metadata allowing filtered exports by topic, date or author.
  
  
• Broad topic coverage: business, finance, technology, politics, health, and global news.
  

Ideal for building derivatives: summarisation, classification, clustering.