On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Reports on the balance sheet and financial indicators of the municipal enterprise "Slyakhrembud" Kharkov. It is no longer renewed. On the basis of the order of the Kharkiv Mayor of 21.10.2021, "On Approval of the Regulations on Data Sets of the Kharkiv City Council", the publication of the open data sets of the information manager on the Kharkiv Open Data Portal was organized, which, according to the Harvesting procedure, are automatically placed on the Unified State Open Data Portal in the office of the manager "Kharkiv City Council". Reports on the balance sheet and financial indicators of the municipal enterprise "Slyakhrembud" Kharkov. It is no longer renewed. On the basis of the order of the Kharkiv Mayor of 21.10.2021, "On Approval of the Regulations on Data Sets of the Kharkiv City Council", the publication of the open data sets of the information manager on the Kharkiv Open Data Portal was organized, which, according to the Harvesting procedure, are automatically placed on the Unified State Open Data Portal in the office of the manager "Kharkiv City Council".

This dataset contains the textual data of Federal Reserve Federal Open Market Committee (FOMC) meeting statements and minutes. Its purpose is to provide a historical archive of communications from the US central bank, offering valuable context and insights into monetary policy decisions and economic outlooks over time. The dataset is regularly updated, ensuring access to the latest official communications.

Columns

• Date: This column specifies the date of the actual FOMC meeting.
• Release Date: This indicates when the statement or minutes were officially released to the public. It is important to note that minutes are typically released with approximately a three-week lag from the meeting date.
• Type: This column categorises the communication, distinguishing between a formal 'statement' and the more detailed 'minutes' of the meeting.
• Text: This contains the full textual content of each communication release, whether it is a statement or minutes.

Distribution

The dataset is typically provided in a CSV (Comma Separated Values) format. It includes communications from 2 February 2000 to 18 June 2025. The file is updated on a weekly basis with new data sourced directly from the Federal Reserve website. Based on available information, there are approximately 420 records within the specified date range. The dataset comprises roughly 52% minutes and 48% statements.

Usage

This dataset is ideal for various applications and use cases, particularly within finance, banking, and economics. It can be used for: * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling on central bank communications. * Economic research to analyse policy shifts, communication strategies, and their impact on financial markets. * Financial modelling and forecasting, by integrating insights from official monetary policy communications. * Academic studies on central banking, macroeconomic policy, and financial history.

Coverage

The dataset covers the period from 2 February 2000 to 18 June 2025, providing an extensive historical record of FOMC communications. While the content focuses on US monetary policy, which is inherently US-centric, the dataset's availability is global, making it accessible to users worldwide. There are no specific notes on data availability for certain demographic groups or years, as the data represents official public releases.

License

CC0

Who Can Use It

This dataset is designed for a wide range of users, including: * Financial analysts and economists seeking to understand and forecast monetary policy decisions. * Data scientists and machine learning engineers developing NLP models for financial text. * Academic researchers in economics, finance, and political science studying central bank behaviour and communication. * Government policy advisors interested in historical policy decisions and their effects. * Journalists and media professionals reporting on economic and financial news.

Dataset Name Suggestions

• Federal Reserve FOMC Communications Archive
• US Monetary Policy Statements and Minutes
• Central Bank Economic Communications
• FOMC Historical Document Collection

Attributes

Original Data Source: FOMC Meeting Statements & Minutes

This table contains information on the balance sheet of the general government sector. The information is limited to financial assets and liabilities. For each reporting period the opening and closing stocks, financial transactions and other changes are shown. Transactions are economic flows that are the result of agreements between units. Other changes are changes in the value of assets or liabilities that do not result from transactions such as revaluations or reclassifications. The figures are consolidated which means that flows between units that belong to the same sector are eliminated. As a result, assets and liabilities of subsectors do not add up to total assets or liabilities of general government. For example, loans of the State provided to social security funds are part of loans of the State. However, these are not included in the consolidated assets of general government, because it is an asset of a government unit with a government unit as debtor. Financial assets and liabilities in this table are presented at market value. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date.

Data available from: Yearly figures from 1995, quarterly figures from 1999.

Status of the figures: The figures for the period 1995-2023 are final. The figures for 2024 and 2025 are provisional.

Changes as of 24 June 2025: The figures for the first quarter of 2025 are available. Figures for 2023 and 2024 have been adjusted due to updated information. The figures for 2023 are final. In the context of the revision policy of National accounts, the dividend tax has been adjusted as of the fourth quarter of 2006. The revised registration aligns more closely with the accrual principle of ESA 2010.

Changes as of 10 April 2025: Due to an error made while processing the data, the initial preliminary figures for the government financial balance sheet in 2024 were calculated incorrectly. This causes a downward revision in other accounts payable.

When will new figures be published? Provisional quarterly figures are published three months after the end of the quarter. In September the figures on the first quarter may be revised, in December the figures on the second quarter may be revised and in March the first three quarters may be revised. Yearly figures are published for the first time three months after the end of the year concerned. Yearly figures are revised two times: 6 and 18 months after the end of the year. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the most actual figures. Revised yearly figures are published in June each year. Quarterly figures are aligned to the three revised years at the end of June. More information on the revision policy of Dutch national accounts and government finance statistics can be found under 'relevant articles' under paragraph 3.

The Ontario Economic Accounts (OEA) is a public document, released four times a year that provides an overall assessment of the current state of the Ontario economy. OEA estimates are based on Statistics Canada data. Its primary audience includes economists in both public and private sectors and credit rating agencies.

*[OEA]: Ontario Economic Accounts

Dataset from Netflix's 10-K annual reports, which include externally audited data about financial activities of businesses based in the US. For a description of the data compiled see the .docx document. The code included was used in the following research:

Title: Evidence of diseconomies of scale in subscription-based video on demand services.

Abstract: This study provides evidence of diseconomies of scale in Netflix, a major subscription-based video on demand (SVOD) service provider. This contradicts the common belief in prevalent economies of scale for such e-businesses. We, however, rely on a comprehensive analysis of a dataset where we have collected and combined publicly available and audited financial data, mostly coming from Netflix's 10-K reports. In our analysis we employ several user-cost models, namely a baseline linear model, a power law model, an exponential model, and a logarithmic model. Such models often appear (in different variations) in economics literature, but are almost inexistent in the rhetoric around SVOD business models. Corroborating the applications of all these mathematical models on the financial data of Netflix identifies a super-linear increase in costs with expanding user basis, indicating the rising per-user costs that defines diseconomies of scale. These findings provide critical insights into SVOD service scalability, challenging prevailing assumptions and informing expectations about cost dynamics in this industry.

The Financial Accounts of the United States includes data on transactions and levels of financial assets and liabilities, by sector and financial instrument; full balance sheets, including net worth, for households and nonprofit organizations, nonfinancial corporate businesses, and nonfinancial noncorporate businesses; Integrated Macroeconomic Accounts; and additional supplemental detail. These data are typically released during the second week of March, June, September, and December.

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This is a collection of data reported in LACERS Annual Financial Report since Fiscal Year End 1990, which includes selective data from the yearly Actuarial Valuation, Audited Financial Statement, and Investments Reporting generated after the end of each Fiscal Year. Average Healthcare Subsidies data are the exception and are pulled from Health division dashboards at fiscal year end. Yearly and multi-year investment return averages are based on time-weighted reporting. Some figures are rounded, as reported in the documents referenced above. Actuarial Valuations and Annual Financial Report documents, in electronic form, can be found on the LACERS.org website at https://www.lacers.org/reports-and-statistics.

On the basis of the order of the Kharkiv Mayor of 21.10.2021, 194 "On Approval of the Regulations on Data Sets of the Kharkiv City Council" the publication of open data sets of the information manager on the Kharkiv Open Data Portal was organized, which, according to the Harvesting procedure, are automatically placed on the Unified State Open Data Portal in the office of the manager of the "Kharkiv City Council"

The Corporate Financial Fraud project is a study of company and top-executive characteristics of firms that ultimately violated Securities and Exchange Commission (SEC) financial accounting and securities fraud provisions compared to a sample of public companies that did not. The fraud firm sample was identified through systematic review of SEC accounting enforcement releases from 2005-2010, which included administrative and civil actions, and referrals for criminal prosecution that were identified through mentions in enforcement release, indictments, and news searches. The non-fraud firms were randomly selected from among nearly 10,000 US public companies censused and active during at least one year between 2005-2010 in Standard and Poor's Compustat data. The Company and Top-Executive (CEO) databases combine information from numerous publicly available sources, many in raw form that were hand-coded (e.g., for fraud firms: Accounting and Auditing Enforcement Releases (AAER) enforcement releases, investigation summaries, SEC-filed complaints, litigation proceedings and case outcomes). Financial and structural information on companies for the year leading up to the financial fraud (or around year 2000 for non-fraud firms) was collected from Compustat financial statement data on Form 10-Ks, and supplemented by hand-collected data from original company 10-Ks, proxy statements, or other financial reports accessed via Electronic Data Gathering, Analysis, and Retrieval (EDGAR), SEC's data-gathering search tool. For CEOs, data on personal background characteristics were collected from Execucomp and BoardEx databases, supplemented by hand-collection from proxy-statement biographies.

This dataset contains the Town's Year-to-Date Budget and Actuals for Fiscal Years 2016 through 2019. Fiscal years run from July 1 to June 30.The data comes from the Town's Enterprise Resource Planning (ERP) software and is subject to change until the year's final audit is complete, which typically occurs by October of the following fiscal year. For example, revenues received may be posted back a previous month or expenditures may be reclassified from one expense category to another throughout the year. This data is maintained in a flexible way to produce a variety of financial reports as required by law, including the Town's annually Adopted Budget and Comprehensive Annual Financial Report (CAFR).These reports can be found on the Town's website through the following links:Town of Chapel Hill Adopted Budget Town of Chapel Hill CAFR

The financial reports for the Department assist assessments of forecast financial performance, and its use of the parliamentary authority for resources.The tables in the spreadsheet display financial performance data via the comprehensive operating statement, the balance sheet, the cash flow statement, the statement of changes in equity, administered items statements, and payments on behalf of the State (where applicable).The datasets should be read in conjunction with Budget Paper No. 3 Service Delivery which provides an overview of the goods and services funded by the Government and delivered by departments in the coming financial year.

Financial Times Interactive Data LLC offers a vast repository of economic and financial data, providing valuable insights into global markets and trading. With a focus on delivering timely and accurate information, the company has established itself as a go-to source for financial institutions, investors, and researchers seeking to stay ahead of the curve.

our vast database is comprised of historic financial statements, economic indicators, and proprietary data from leading sources, including government agencies, regulatory bodies, and industry associations. By providing access to this trove of information, Financial Times Interactive Data LLC enables its clients to make informed decisions, identify trends, and uncover new opportunities in the rapidly evolving world of finance.

Comprehensive database of over 100,000 financial filings from 8,000+ European companies

Lucror Analytics: Fundamental Fixed Income Data and Financial Models for High-Yield Bond Issuers

At Lucror Analytics, we deliver expertly curated data solutions focused on corporate credit and high-yield bond issuers across Europe, Asia, and Latin America. Our data offerings integrate comprehensive fundamental analysis, financial models, and analyst-adjusted insights tailored to support professionals in the credit and fixed-income sectors. Covering 400+ bond issuers, our datasets provide a high level of granularity, empowering asset managers, institutional investors, and financial analysts to make informed decisions with confidence.

By combining proprietary financial models with expert analysis, we ensure our Fixed Income Data is actionable, precise, and relevant. Whether you're conducting credit risk assessments, building portfolios, or identifying investment opportunities, Lucror Analytics offers the tools you need to navigate the complexities of high-yield markets.

What Makes Lucror’s Fixed Income Data Unique?

Comprehensive Fundamental Analysis Our datasets focus on issuer-level credit data for complex high-yield bond issuers. Through rigorous fundamental analysis, we provide deep insights into financial performance, credit quality, and key operational metrics. This approach equips users with the critical information needed to assess risk and uncover opportunities in volatile markets.

Analyst-Adjusted Insights Our data isn’t just raw numbers—it’s refined through the expertise of seasoned credit analysts with 14 years average fixed income experience. Each dataset is carefully reviewed and adjusted to reflect real-world conditions, providing clients with actionable intelligence that goes beyond automated outputs.

Focus on High-Yield Markets Lucror’s specialization in high-yield markets across Europe, Asia, and Latin America allows us to offer a targeted and detailed dataset. This focus ensures that our clients gain unparalleled insights into some of the most dynamic and complex credit markets globally.

How Is the Data Sourced? Lucror Analytics employs a robust and transparent methodology to source, refine, and deliver high-quality data:

• Public Sources: Includes issuer filings, bond prospectuses, financial reports, and market data.
• Proprietary Analysis: Leveraging proprietary models, our team enriches raw data to provide actionable insights.
• Expert Review: Data is validated and adjusted by experienced analysts to ensure accuracy and relevance.
• Regular Updates: Models are continuously updated to reflect market movements, regulatory changes, and issuer-specific developments.

This rigorous process ensures that our data is both reliable and actionable, enabling clients to base their decisions on solid foundations.

Primary Use Cases 1. Fundamental Research Institutional investors and analysts rely on our data to conduct deep-dive research into specific issuers and sectors. The combination of raw data, adjusted insights, and financial models provides a comprehensive foundation for decision-making.

1. Credit Risk Assessment Lucror’s financial models provide detailed credit risk evaluations, enabling investors to identify potential vulnerabilities and mitigate exposure. Analyst-adjusted insights offer a nuanced understanding of creditworthiness, making it easier to distinguish between similar issuers.

2. Portfolio Management Lucror’s datasets support the development of diversified, high-performing portfolios. By combining issuer-level data with robust financial models, asset managers can balance risk and return while staying aligned with investment mandates.

3. Strategic Decision-Making From assessing market trends to evaluating individual issuers, Lucror’s data empowers organizations to make informed, strategic decisions. The regional focus on Europe, Asia, and Latin America offers unique insights into high-growth and high-risk markets.

Key Features of Lucror’s Data - 400+ High-Yield Bond Issuers: Coverage across Europe, Asia, and Latin America ensures relevance in key regions. - Proprietary Financial Models: Created by one of the best independent analyst teams on the street. - Analyst-Adjusted Data: Insights refined by experts to reflect off-balance sheet items and idiosyncrasies. - Customizable Delivery: Data is provided in formats and frequencies tailored to the needs of individual clients.

Why Choose Lucror Analytics? Lucror Analytics and independent provider free from conflicts of interest. We are committed to delivering high-quality financial models for credit and fixed-income professionals. Our proprietary approach combines proprietary models with expert insights, ensuring accuracy, relevance, and utility.

By partnering with Lucror Analytics, you can: - Safe costs and create internal efficiencies by outsourcing a highly involved and time-consuming processes, including financial analysis and modelling. - Enhance your credit risk ...

At the end of each fiscal year, the Receiver general of Canada publishes financial information in the Public Accounts. This dataset, based on the related table in section 9 of volume I, presents (in thousands of dollars) a combined summary of the financial statements of enterprise Crown corporations and other government business enterprises by segment. This non-official record of information comes from the Public Accounts of Canada. You can find the official version for the most recent fiscal year on the Receiver General website and that of Library and Archives for historical years. Note: This dataset covers years 2007-2016. Following 2016, the Public Accounts of Canada are no longer collecting and publishing this data due to changes in requirements by TBS.

The Australian Government general government sector Monthly Financial Statements are officially available from July to May for each month at www.finance.gov.au.

This dataset provides an historical series of a collection of published Australian Government general government sector monthly financial statements from 2005-06, including the:

• Aggregates tables

• Operating Statement

• Balance Sheet

• Cash Flow Statement

• Taxation tables

• Function tables

Monthly Financial Statements are not published for the month of June. These figures can be sourced from the Final Budget Outcome (www.budget.gov.au) or the Consolidated Financial Statements (www.finance.gov.au).

The Historical Monthly Financial Statements series is provided to assist those who wish to analyse, visualise and programmatically access this data.

The Australian Government Monthly Financial Statements are prepared on a basis consistent with the Budget as required under section 47 of the Public Governance, Performance and Accountability Act 2013 (formerly section 54 of the Financial Management and Accountability Act 1997).

Since 2008-09 the statements have been prepared in accordance with Australian Accounting Standard AASB 1049 – Whole of Government and General Government Sector Financial Reporting, which requires accounting treatment based on the Australian Bureau of Statistics’ (ABS) Government Finance Statistics (GFS) except where Australian Accounting Standards (AAS) provide a better conceptual treatment for specific items. Departures are limited to complying with either ABS GFS or AAS. The change in 2008-09 represented a significant change in the format and content of the monthly financial statements.

The Monthly Financial Statements estimates dataset is based solely upon the published monthly profiles. Where no year-to-date profile was published the year-to-date actual figure has been used in its place.

Please note that this dataset represents published information and will not be recast. Figures may not be directly comparable over time due to changes of classification, accounting standards or budget treatments.

This data is released by the Department of Finance.

The financial reports for the Department assist assessments of forecast financial performance, and its use of the parliamentary authority for resources.The tables in the spreadsheet display …Show full descriptionThe financial reports for the Department assist assessments of forecast financial performance, and its use of the parliamentary authority for resources.The tables in the spreadsheet display financial performance data via the comprehensive operating statement, the balance sheet, the cash flow statement, the statement of changes in equity, administered items statements, and payments on behalf of the State (where applicable).The datasets should be read in conjunction with Budget Paper No. 3 Service Delivery which provides an overview of the goods and services funded by the Government and delivered by departments in the coming financial year.