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

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

Context:

The People with significant control (PSC) snapshot is a data snapshot containing the full list of PSC's provided to Companies House. The Prime Minister first put corporate transparency on the international agenda when he chaired the G8 summit in Lough Erne and secured commitment to action, the commitment to enhance corporate transparency in the UK was reaffirmed at London’s International Anti-Corruption Summit in May 2016. Since then the EU and G20 countries have also agreed to act. The UK is the first country in the G20 to create a public register of this kind.

The UK has high standards of business behaviour and corporate governance. The overwhelming majority of UK companies contribute productively to the UK economy, abide by the law and make a valuable contribution to society. But there are exceptions. Some of the features of the company structure which make it good for business also make it attractive to criminals. Companies can be misused to facilitate a range of criminal activities - from money laundering to tax evasion, corruption to terrorist financing. Sometimes those individuals running companies will not conduct themselves in accordance with the high standards we expect in the UK, posing a risk to other companies and consumers alike.

Information about the ownership and control of UK corporate entities will bring benefits for law enforcement, business, civil society and citizens. By making this information publicly available, free of charge, the government is setting a standard that we are persuading other countries to follow.

Content:

A person of significant control is someone that holds more than 25% of shares or voting rights in a company, has the right to appoint or remove the majority of the board of directors or otherwise exercises significant influence or control. This is a snapshot of data in zipped JSON form, as of Aug 23 2017. Daily updated snapshots and streaming API details can be found here. The People with Significant Control (PSC) register includes information about the individuals who own or control companies including their name, month and year of birth, nationality, and details of their interest in the company. From 30 June 2016, UK companies (except listed companies) and limited liability partnerships (LLPs) need to declare this information when issuing their annual confirmation statement to Companies House.

Acknowledgements:

Guidance here. The data is collected by UK government.

Inspiration:

• Who owns the most businesses? In certain areas?
• Any weird looking situations where ownership might be obscured?

The Free Company Data Product is a downloadable data snapshot containing basic company data of live companies on the register. http://download.companieshouse.gov.uk/en_output.html

The Free Company Data Product is a downloadable data snapshot containing basic company data of live companies on the register. This dataset is just for Calderdale companies. The full download is here -http://download.companieshouse.gov.uk/en_output.html A list of the data fields contained in the snapshot can be found here - http://resources.companieshouse.gov.uk/toolsToHelp/pdf/freeDataProductDataset.pdf

I work with UK company information on a daily basis, and I thought it would be useful to publish a list of all active companies, in a way that could be used for machine learning.

There are 3,838,469 rows in the dataset, one for each active company. Each row, has the company name, date of incorporation and the Standard Industrial Classification Code.

The company list is from the publicly available 1st November 2017 Companies House snapshot.

The SIC code descriptions are from the gov.uk website.

In the file AllCompanies.csv each row is formatted as follows:

• CompanyName - Alpha numberic company name
• IncorporationDate - in British date format, dd/mm/yyyy
• SIC - 5 digits or if not known, None - see separate file for description of each code.

Inspiration

Possible uses for this data is to use ML to suggest a new unique but suitable name for a company based on what other companies of the same SIC are called.

Perhaps analyse how company names have evolved over time.

Using ML, perhaps determine what a typical company name looks like, maybe analyse if company names have got longer or more complicated over time.

I am sure there are many more possible uses for this data in ways, that I cannot imagine.

This is my second go (the first was published a few hours ago) at publishing a dataset on any medium, so any useful tips and hints would be extremely welcome.

Links to the raw data sources are here:

• Companies House http://download.companieshouse.gov.uk/en_output.html
• SIC Codes https://www.gov.uk/government/publications/standard-industrial-classification-of-economic-activities-sic

A list of businesses located in London showing a range of information including company name, address, postcode, local authority, and SIC code (industry).

The Free Company Data Product is a downloadable data snapshot containing basic company data of live companies on the register. The version available from Companies House has all UK business and is available to download in a series of Zip folders.

The London cut available to download here, was created using a postcode list. Therefore, if there is an error in the postcode, or some other problem caused the postcode listed not to match a London postcode, those business will not be included in the file. Furthermore, inaccuracies in postcodes may mean that no local authority is listed for a company.

Each entry represents a financial accounts submission of either a whole company or part of one. Some businesses have more than one entry in the directory because they need to submit more than one set of accounts for different parts of their business. Therefore, the number of entries in the directory will be greater than the number of businesses.

Note, Large file size - the Excel file is almost 600MB.

Companies House update the latest snapshot within 5 working days of the previous month end. The London file available here was published on 1 May 2018.

Further metadata and user guidance is available here.

HM Land Registry’s UK Companies data contains entries made in the Title Register for property in England and Wales owned by Companies incorporated in the UK. The data includes the following ownership types: corporations aggregate, county councils, other local authorities, housing associations, industrial and provident societies, registered societies, limited companies, public limited companies, unlimited companies, limited liability partnerships, community interest companies, Societas Europaea (where registered at Companies House), UK companies with an overseas correspondence address. It is available for download as monthly files and contains approximately 3.2 million records

Graph and download economic data for Finance Companies; Home Equity Lines of Credit; Asset, Transactions (BOGZ1FA613065200A) from 1971 to 2024 about HELOCs, home equity, finance companies, companies, finance, credits, transactions, financial, assets, and USA.

Graph and download economic data for Finance Companies; Home Equity Lines of Credit; Asset, Level (BOGZ1FL613065200Q) from Q2 1970 to Q2 2025 about HELOCs, home equity, finance companies, companies, finance, credits, financial, assets, and USA.

Context

In the United Kingdom, it's mandated by law that any organization employing over 250 individuals must disclose their gender pay gap data annually. This is not to be mistaken with equal pay, which is the legal obligation to pay men and women the same for equivalent work. The gender pay gap, on the other hand, is a broader measure that looks at the average differences in pay, seniority, and career advancement between male and female employees. This makes it a more potent indicator of gender equality and institutional bias within organizations.

Content

Geography: United Kingdom

Time period: 2018-2023

Unit of analysis: UK Gender Pay Gap

Dataset: This dataset currently contains data collected by the Gender Pay Gap Service for the 2018 to 2023 reporting years. More data will be added as it becomes available.

At present, the Gender Pay Gap Service only provides data downloads in CSV format, divided by the reporting year. This dataset amalgamates all the available CSV files, with column descriptions and file introductions informed by my firsthand experience working on the Gender Pay Gap Service website for the Government Equalities Office.

Variables

Data collection comprising information about public sector procurement from Local Authorities in the UK and Clinical Commissioning Groups in England from 2013-15, funding awards made by foundations and charitable funders, and data on recipient organisations, mainly charities and social enterprises. Data were sourced from a large number of local authorities and Clinical Commissioning Groups by downloading the datasets from authority websites. Each of the datasets were then cleaned, including identifying missing or incomplete rows andconverting dates into a standard format. This data collection represents a formidable resource which has the potential to inform analyses of the distribution of funding to third sector organisations in the form of grants from foundations, government and others; to support further analytical work on the effects of such grants on the subsequent financial trajectories of organisations; to underpin investigations of the extent to which third sector organisations are in receipt of contracts from public sector agencies; and to analyse the balance between public and private provision of welfare services.

The aim of this project is to provide comprehensive data on funding flows to third sector organisations in England. We exploit the growing body of data being made available on grant making to voluntary organisations, and on public procurement, to build resources which will allow researchers to improve understanding of the funding mix of third sector organisations. The resources we create will help third sector organisations and policy makers because it will improve their understanding of their environment, particularly funding opportunities and also potential competitors or collaborators. This means they will be able to better support their beneficiaries by, for example, targeting their services or co-operating with similar organisations. The data will meet administrative and research challenges facing the sector by providing a "spine" for the growing number of open data initiatives. This new data infrastructure will add new value to existing UK social science infrastructure, and will be able to be reused by academic and non-academic researchers (such as those working in government). This data will also help the Office for National Statistics as they work to improve coverage of non-profit organisations in the National Accounts (see letters of support). It is consistent with ESRC's strategic priorities such as a "Vibrant and fair society", in that it significantly enhances understanding of the resources of third sector organisations. Building on TSRC/NCVO's existing databases, generated by combining registers of charities and the Companies House register of companies, we have developed novel data sources for the third sector in the following way. 1. we will download local authority and clinical commissioning group (CCG) data for England, usually available from relevant authorities on a monthly basis; 2. match the CCG data to our existing third sector databases using string matching techniques, and make available the results; 3. match the local authority procurement data to the same databases; 4. capture listings of grants made by charitable funders to organisations in England, such as those lists of grant recipients collated by the "360 Giving"; initiative which is encouraging grant makers to open up data about who they fund but we will work with other major funders. Published data already includes several hundred thousand awards made to voluntary organisations. the sheer scale and ambition of this project is unprecedented anywhere in the world. it provides much greater granular data on relationships between public sector agencies and those from whom they are commissioning services than is possible anywhere else. The project represents an excellent example of a partnership between a strong academic research centre, TSRC, and a high-profile national voluntary organisation, NCVO, in which research is designed and developed with the needs of user and academic communities equally in mind.

New Homeowner Data is a subset of our comprehensive property intelligence database that can be segmented by specific property criteria, household demographics, mortgage, and real estate portfolio information.

Companies in the home services, financial products, and consumer products industries use BatchData to identify new homeowners who have purchased a property in the last 90 days and uncover their direct phone number, email, and mailing address for timely marketing of products and services new homeowners need. New homeowner data can also be segmented property type (residential real estate or commercial real estate), length of ownership, owner occupancy status, and more!

New homeowner data is available in a variety of data delivery and data enrichment modes: API (you pull data from us using an API), webhook (we push data to you using an API), AWS S3 upload (we deliver the data to you), S3 download (you download the data from our S3 bucket), SFTP.

BatchData is both a data and technology solution helping companies in and around the real estate ecosystem achieve faster growth. BatchData specializes in providing accurate contact information for US property owners, including in-depth intelligence and actionable insights related to their property. Our portfolio of products, services, and go-to-market expertise help companies identify their target market, reach the right prospects, enrich their data, and power their products and services.

Graph and download economic data for Finance Companies; Home Equity Lines of Credit; Asset, Transactions (BOGZ1FU613065200Q) from Q3 1970 to Q2 2025 about HELOCs, home equity, finance companies, companies, finance, credits, transactions, financial, assets, and USA.

Graph and download economic data for Finance Companies; Home Equity Loans; Asset, Level (BOGZ1FL613065123A) from 1970 to 2024 about home equity, finance companies, companies, finance, financial, assets, and USA.

Graph and download economic data for Finance Companies; Home Equity Loans; Asset, Transactions (BOGZ1FA613065123Q) from Q3 1970 to Q2 2025 about home equity, finance companies, companies, finance, transactions, financial, assets, and USA.

Graph and download economic data for Finance Companies; Home Equity Loans; Asset, Level (BOGZ1FL613065123Q) from Q2 1970 to Q2 2025 about home equity, finance companies, companies, finance, financial, assets, and USA.

Graph and download economic data for Finance Companies; Home Equity Loans; Asset, Revaluation (BOGZ1FR613065123A) from 1971 to 2024 about home equity, revaluation, finance companies, companies, finance, financial, assets, and USA.

Graph and download economic data for Finance Companies; Construction Loans on One-to-Four Family Homes; Asset, Transactions (BOGZ1FA613065183Q) from Q4 1946 to Q2 2025 about finance companies, companies, finance, transactions, family, financial, construction, assets, housing, and USA.