Monthly state sales tax collections is an experimental dataset published by the U.S. Census Bureau. It provides data for collections from sales taxes including motor fuel taxes. Data reported for a specific month generally represent sales taxes collected on sales made during the prior month. Tax collections primarily rely on unaudited data collected from existing state reports or state data sources available from and posted on the Internet. Secondarily, states report the data via the Quarterly Survey of State and Local Tax Revenue. Data are updated monthly, but due to differing reporting cycles data for some states may lag.

Daily overview of federal revenue collections such as income tax deposits, customs duties, fees for government service, fines, and loan repayments.

Graph and download economic data for National Totals of State and Local Tax Revenue: Total Taxes for the United States (QTAXTOTALQTAXCAT1USNO) from Q1 1992 to Q2 2025 about state & local, revenue, tax, government, and USA.

This dataset contains revenue source level data for revenue actuals. Dataset is intended to match charts and tables in the "Tax Revenue" section of the Mayor`s Message publication. The amount is in millions of dollars. Data are from FY2001 and updated once a year.

The Corporate Tax Rate in the United States stands at 21 percent. This dataset provides - United States Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table shows the gross receipts, refunds and net receipts for the current month, the current fiscal year-to-date and the prior fiscal year-to-date for the various receipts of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for functions of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

The Collecting Taxes Database contains performance and structural indicators about national tax systems. The database contains quantitative revenue performance indicators, such as how well a particular tax performs in generating revenues for the treasury, given its overall rate structure, and how well the overall tax system produces revenues, given the costs of administering the tax system. The database also provides tax rate information, such as the general VAT rate or the general corporate income tax rate. Other indicators describe the main features of tax administrations and economic indicators are included so that performance, rate competitiveness, and structure can be compared given the levels of country development and other factors.

Context

Form 990 (officially, the "Return of Organization Exempt From Income Tax"1) is a United States Internal Revenue Service form that provides the public with financial information about a nonprofit organization. It is often the only source of such information. It is also used by government agencies to prevent organizations from abusing their tax-exempt status. Source: https://en.wikipedia.org/wiki/Form_990

Content

Form 990 is used by the United States Internal Revenue Service to gather financial information about nonprofit/exempt organizations. This BigQuery dataset can be used to perform research and analysis of organizations that have electronically filed Forms 990, 990-EZ and 990-PF. For a complete description of data variables available in this dataset, see the IRS’s extract documentation: https://www.irs.gov/uac/soi-tax-stats-annual-extract-of-tax-exempt-organization-financial-data.

Update Frequency: Annual

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:irs_990

https://cloud.google.com/bigquery/public-data/irs-990

Dataset Source: U.S. Internal Revenue Service. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What organizations filed tax exempt status in 2015?

What was the revenue of the American Red Cross in 2017?

USAID's Collecting Taxes Database (CTD) is a compilation of international statistics about taxation designed to provide policymakers, practitioners, and researchers with the means to conduct analysis on domestic revenue mobilization (DRM). It is part of a wider agenda of the international community to help countries strengthen their tax systems and mobilize domestic revenue.

The CTD includes information on tax performance and tax administration variables for 200 countries and territories. USAID plans to update the CTD annually.

The CTD comprises a set of 30 indicators divided into three main categories -- (1) Tax Rates and Structure; (2) Tax Performance; and (3) Tax Administration -- and includes information on 200 national tax systems. The tax rates and structure indicators capture historical tax rates and thresholds. The tax performance indicators measure how effectively the tax system produces revenues.

The CTD Performance Data includes Tax Rates and Structure and Tax Performance data.

This data contains boundaries for local general sales and use tax areas in Minnesota, along with rates and descriptions for each area. It was developed as a geographical representation of the boundaries of local general sales and use tax rates in Minnesota.

The data is updated each quarter as boundaries change and local jurisdictional units add or discontinue the implementation of local general sales and use taxes. This resource will always include the current quarter's taxing area boundaries. Updates are published thirty days in advance of the beginining of the quarter.

This dataset DOES NOT include special local taxes that may exist (lodging, entertainment, liquor, admissions and restaurant taxes).

To learn more about sales and use tax collected by the Minnesota Department of Revenue, visit this page.
https://www.revenue.state.mn.us

This table shows the gross outlays, applicable receipts and net outlays for the current month, current fiscal year-to-date and prior fiscal year-to-date by various agency programs accounted for in the budget of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of the Change in Excess of Liabilities. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This dataset explores the U.S.Census data on State Government Tax Collections (STC) for the year 2007, by state and type of tax. The State Government Tax Collections (STC) report lists the various taxes collected by each state. The tables and data files show the tax revenues collected on a state by state basis.

We develop a dataset of state (1977–2022) and local (2000–2022) tax rates from personal income, corporate income, property, sales, and excise taxes. We utilize sources such as NBER TaxSim, Tax Foundation, CCH CorpSystem, state and local level tax authority websites (typically the department of revenue or equivalent body), as well as data from other research. For each state and local tax type, we collect annual data going back in time as far possible. Local jurisdictions include everything from city and county governments to overlapping taxing geographies defined by local school boundaries, water and fire districts, or even specially constructed business tax districts. We aggregate local tax rates to the county level as this represents a stable and well-defined geographic mapping that is non-overlapping and corresponds well to other available measures of governmental oversight and economic activity.

This table shows the receipts and outlays of the United States Government by month for the current fiscal year, up to and including the current accounting month. The table also shows the total receipts and outlays for the current fiscal year-to-date and the comparable prior fiscal year-to-date. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This annual study provides county income and tax data (and State totals) that include the number of returns, which approximates the number of households; number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based on the addresses reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its annual study on Individual Tax Return Statistics by Geographic Areas, County Data.

According to Cognitive Market Research, the global AI Training Dataset Market size will be USD 2962.4 million in 2025. It will expand at a compound annual growth rate (CAGR) of 28.60% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1096.09 million in 2025 and will grow at a compound annual growth rate (CAGR) of 26.4% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue, with a market size of USD 859.10 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 710.98 million in 2025 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2025 to 2033.
South America has a market share of more than 3.8% of the global revenue, with a market size of USD 112.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.6% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 118.50 million in 2025 and will grow at a compound annual growth rate (CAGR) of 27.9% from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 65.17 million in 2025 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2025 to 2033.
Data Annotation category is the fastest growing segment of the AI Training Dataset Market

Market Dynamics of AI Training Dataset Market

Key Drivers for AI Training Dataset Market

Government-Led Open Data Initiatives Fueling AI Training Dataset Market Growth

In recent years, Government-initiated open data efforts have strongly driven the development of the AI Training Dataset Market through offering affordable, high-quality datasets that are vital in training sound AI models. For instance, the U.S. government's drive for openness and innovation can be seen through portals such as Data.gov, which provides an enormous collection of datasets from many industries, ranging from healthcare, finance, and transportation. Such datasets are basic building blocks in constructing AI applications and training models using real-world data. In the same way, the platform data.gov.uk, run by the U.K. government, offers ample datasets to aid AI research and development, creating an environment that is supportive of technological growth. By releasing such information into the public domain, governments not only enhance transparency but also encourage innovation in the AI industry, resulting in greater demand for training datasets and helping to drive the market's growth.

India's IndiaAI Datasets Platform Accelerates AI Training Dataset Market Growth

India's upcoming launch of the IndiaAI Datasets Platform in January 2025 is likely to greatly increase the AI Training Dataset Market. The project, which is part of the government's ?10,000 crore IndiaAI Mission, will establish an open-source repository similar to platforms such as HuggingFace to enable developers to create, train, and deploy AI models. The platform will collect datasets from central and state governments and private sector organizations to provide a wide and rich data pool. Through improved access to high-quality, non-personal data, the platform is filling an important requirement for high-quality datasets for training AI models, thus driving innovation and development in the AI industry. This public initiative reflects India's determination to become a global AI hub, offering the infrastructure required to facilitate startups, researchers, and businesses in creating cutting-edge AI solutions. The initiative not only simplifies data access but also creates a model for public-private partnerships in AI development.

Restraint Factor for the AI Training Dataset Market

Data Privacy Regulations Impeding AI Training Dataset Market Growth

Strict data privacy laws are coming up as a major constraint in the AI Training Dataset Market since governments across the globe are establishing legislation to safeguard personal data. In the European Union, explicit consent for using personal data is required under the General Data Protection Regulation (GDPR), reducing the availability of datasets for training AI. Likewise, the data protection regulator in Brazil ordered Meta and others to stop the use of Brazilian personal data in training AI models due to dangers to individuals' funda...

This summary table shows the on-budget and off-budget receipts and outlays, the on-budget and off-budget surplus/deficit, and the means of financing the budget surplus/deficit. The table also shows the budgeted amounts estimated in the President's Budget for the current fiscal year and next fiscal year for each item on the table. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

Abstract copyright UK Data Service and data collection copyright owner.The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material. The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files. Main Topics: Each record is the income or expenditure of an individual tax for a given year and treasury. Variables Archival source; starting month; starting year; ending month; ending year; caja id; tax code number; type (debit or credit); pesos de ocho; pesos de ensayados; pesos de oro; tax name. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe)