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.

The main data files of 2010-2020 US state + DC corporate (top/max), personal (top/max), and sales tax rates are State_Taxes.dta in Stata dta format, and State_Taxes.csv, which is the same, but converted to a csv file.

For more details concerning variables and sources, see Readme.md.

Geospatial data about Missouri Sales Tax Boundaries. Export to CAD, GIS, PDF, CSV and access via API.

HitHorizons Dataset of VAT and Tax Numbers gives access to aggregated data on 80M+ companies from the whole of Europe and beyond.

Company registration data: company name national identifier and its type registered address: street, postal code, city, state / province, country business activity: SIC code, local activity code with classification system year of establishment company type location type

Sales and number of employees data: sales in EUR, USD and local currency (with local currency code) total number of employees sales and number of employees accuracy local number of employees (in case of multiple branches) companies’ sales and number of employees market position compared to other companies in a country / industry / region

Industry data: size of the whole industry size of all companies operating within a particular SIC code benchmarking within a particular country or industry regional benchmarking (EU 27, state / province)

Contact details: company website company email domain (without person’s name)

Invoicing details available for selected countries: company name company address company VAT number

This is a collection of CSV files that contain assessment data. The files in this extract are: Primary Parcel file containing primary owner and land information; Addn file containing drawing vectors for dwelling records; Additional Address file containing any additional addresses that exist for a parcel; Assessment file containing assessed value-related data; Appraisal file containing appraised value-related data; Commercial file containing primary commercial data; Commercial Apt containing commercial apartment data; Commercial Interior Exterior data Dwelling file Entrance data containing data from appraisers' visits; Other Buildings and Yard Improvements Sales File Tax Rate File for the current billing cycle by taxing district authority and property class; and, Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included: Data Dictionary PDF; and, St Louis County Rate Book for the current tax billing cycle.

The ATO (Australian Tax Office) made a dataset openly available (see links) showing all the Australian Salary and Wages (2002, 2006, 2010, 2014) by detailed occupation (around 1,000) and over 100 SA4 regions. Sole Trader sales and earnings are also provided. This open data (csv) is now packaged into a database (*.sql) with 45 sample SQL queries (backupSQL[date]_public.txt).See more description at related Figshare #datavis record. Versions:V5: Following #datascience course, I have made main data (individual salary and wages) available as csv and Jupyter Notebook. Checksum matches #dataTotals. In 209,xxx rows.Also provided Jobs, and SA4(Locations) description files as csv. More details at: Where are jobs growing/shrinking? Figshare DOI: 4056282 (linked below). Noted 1% discrepancy ($6B) in 2010 wages total - to follow up.#dataTotals - Salary and WagesYearWorkers (M)Earnings ($B) 20028.528520069.4372201010.2481201410.3584#dataTotal - Sole TradersYearWorkers (M)Sales ($B)Earnings ($B)20020.9611320061.0881920101.11122620141.19630#links See ATO request for data at ideascale link below.See original csv open data set (CC-BY) at data.gov.au link below.This database was used to create maps of change in regional employment - see Figshare link below (m9.figshare.4056282).#packageThis file package contains a database (analysing the open data) in SQL package and sample SQL text, interrogating the DB. DB name: test. There are 20 queries relating to Salary and Wages.#analysisThe database was analysed and outputs provided on Nectar(.org.au) resources at: http://118.138.240.130.(offline)This is only resourced for max 1 year, from July 2016, so will expire in June 2017. Hence the filing here. The sample home page is provided here (and pdf), but not all the supporting files, which may be packaged and added later. Until then all files are available at the Nectar URL. Nectar URL now offline - server files attached as package (html_backup[date].zip), including php scripts, html, csv, jpegs.#installIMPORT: DB SQL dump e.g. test_2016-12-20.sql (14.8Mb)1.Started MAMP on OSX.1.1 Go to PhpMyAdmin2. New Database: 3. Import: Choose file: test_2016-12-20.sql -> Go (about 15-20 seconds on MacBookPro 16Gb, 2.3 Ghz i5)4. four tables appeared: jobTitles 3,208 rows | salaryWages 209,697 rows | soleTrader 97,209 rows | stateNames 9 rowsplus views e.g. deltahair, Industrycodes, states5. Run test query under **#; Sum of Salary by SA4 e.g. 101 $4.7B, 102 $6.9B#sampleSQLselect sa4,(select sum(count) from salaryWageswhere year = '2014' and sa4 = sw.sa4) as thisYr14,(select sum(count) from salaryWageswhere year = '2010' and sa4 = sw.sa4) as thisYr10,(select sum(count) from salaryWageswhere year = '2006' and sa4 = sw.sa4) as thisYr06,(select sum(count) from salaryWageswhere year = '2002' and sa4 = sw.sa4) as thisYr02from salaryWages swgroup by sa4order by sa4

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

 Primary Parcel file containing primary owner and land information; Addn file containing drawing vectors for dwelling records; Additional Address file containing any additional addresses that exist for a parcel; Assessment file containing assessed value-related data; Appraisal file containing appraised value-related data; Commercial file containing primary commercial data; Commercial Apt containing commercial apartment data; Commercial Interior Exterior data Dwelling file Entrance data containing data from appraisers' visits; Other Buildings and Yard Improvements Sales File Tax Rate File for the current billing cycle by taxing district authority and property class; and, Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included: Data Dictionary PDF; and, St Louis County Rate Book for the current tax billing cycle.