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The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

Comprehensive dataset of 15 Ladder suppliers in California, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 12 Ladder suppliers in New York, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The structure of the data in a mixed database can be a barrier when clustering that database into meaningful groups. A hierarchically structured database necessitates efficient distance measures and clustering algorithms to locate similarities between data objects. Therefore, existing literature proposes hierarchical distance measures to measure the similarities between the records in hierarchical databases. The main contribution of this research is to create and test a new distance measure for large hierarchical databases consisting of mixed data types and attributes, based on an existing tree-based (hierarchical) distance metric, the pq-gram distance metric. Several aims and objectives were pursued to fill a number of gaps in the current body of knowledge. One of these goals was to verify the validity of the pq-gram distance metric when applied to different data sets, and to compare and combine it with a number of different distance measures to demonstrate its usefulness across large mixed databases. To achieve this, further work focused on exploring how to exploit the existing method as a measure of hierarchical data attributes in mixed data sets, and to ascertain whether the new method would produce better results with large mixed databases. For evaluation purposes, the pq-gram metric was applied to The Health Improvement Network (THIN) database to determine if it could identify similarities between the records in the database. After this, it was applied to mixed data to examine different distance measures, which include non-hierarchical and other hierarchical measures, and to combine them to create a Combined Distance Function (CDF). The CDF improved the results when applied to different data sets, such as the hierarchical National Bureau of Economic Research of United States (NBER US) Patent data set and the mixed (THIN) data set. The CDF was then modified to create a New-CDF, which used only the hierarchical pq-gram metric to measure the hierarchical attributes in the mixed data set. The New-CDF worked well, finding the most similar data records when applied to the THIN data set, and grouping them in one cluster using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering algorithm. The quality of the clusters was explored using two internal validation indices, Silhouette and C-Index, where the values showed good compactness and quality of the clusters obtained using the new method.

United States Life Insurers: Assets Size by Organizational Structure data was reported at 8,743.195 USD bn in 2023. This records an increase from the previous number of 8,274.918 USD bn for 2022. United States Life Insurers: Assets Size by Organizational Structure data is updated yearly, averaging 6,992.794 USD bn from Dec 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 8,743.195 USD bn in 2023 and a record low of 5,492.658 USD bn in 2011. United States Life Insurers: Assets Size by Organizational Structure data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG002: Life Insurance: Size of Insurers.

With this dataset, you get access to names of officers – individuals appointed by the board of directors to manage the day-to-day operations of a company, and shareholders – individuals or entities that own shares in a company.

This dataset can be a great resource for understanding the corporate hierarchy, analyzing ownership structures, conducting due diligence, or evaluating potential business partnerships.

United States Life Insurers: Premium Income Size by Organizational Structure: Fraternal data was reported at 12.240 USD bn in 2023. This records an increase from the previous number of 10.726 USD bn for 2022. United States Life Insurers: Premium Income Size by Organizational Structure: Fraternal data is updated yearly, averaging 10.429 USD bn from Dec 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 12.240 USD bn in 2023 and a record low of 7.727 USD bn in 2021. United States Life Insurers: Premium Income Size by Organizational Structure: Fraternal data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG002: Life Insurance: Size of Insurers.

Snapshot of the Interdepartmental Business Register.

Comprehensive dataset of 13 Ladder suppliers in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Understanding job titles, career trajectories, and promotions provides valuable insight into labor market dynamics and professional mobility. We present Career Map (CMap), a novel dataset spanning 24 industry sectors, systematically structured to study job specialization, sector concentration, and career advancements. Using advanced natural language processing techniques and large language models, we standardize 6.2 million job titles into 109 thousand unique titles and introduce a Specialization Index to quantify how specialized a title is within its sector. The dataset includes both a structured job titles dataset and a set of identified promotions—30 thousand validated promotions from the United States and the United Kingdom, and 72 thousand inferred promotions from a global context. It enables research on job hierarchies, workforce mobility and systemic inequalities in professional advancement. By providing insights into career progression patterns, labor market structures, and the impact of education and experience, this dataset serves as a valuable resource for economists, sociologists, and computational researchers studying employment trends across industries and regions.This repository contains the code necessary to recreate Figure 4 and Table 4 from the original manuscript.

United States Life Insurers: Assets Size by Organizational Structure: Fraternal data was reported at 203.290 USD bn in 2023. This records an increase from the previous number of 195.778 USD bn for 2022. United States Life Insurers: Assets Size by Organizational Structure: Fraternal data is updated yearly, averaging 172.877 USD bn from Dec 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 203.290 USD bn in 2023 and a record low of 122.982 USD bn in 2011. United States Life Insurers: Assets Size by Organizational Structure: Fraternal data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG002: Life Insurance: Size of Insurers.

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United States Life Insurers: Life Insurers Purchased Size by Organizational Structure: Other data was reported at 34.698 USD bn in 2023. This records an increase from the previous number of 31.789 USD bn for 2022. United States Life Insurers: Life Insurers Purchased Size by Organizational Structure: Other data is updated yearly, averaging 26.612 USD bn from Dec 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 34.698 USD bn in 2023 and a record low of 22.476 USD bn in 2012. United States Life Insurers: Life Insurers Purchased Size by Organizational Structure: Other data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG002: Life Insurance: Size of Insurers.

Abstract copyright UK Data Service and data collection copyright owner. The Annual Business Survey (ABS) is an annual survey of businesses covering the production, construction, distribution and service industries, which represents about two-thirds of the UK economy in terms of Gross Value Added (GVA). Every year, ABS questionnaires are sent by the Office for National Statistics (ONS) to around 62,000 businesses in Great Britain, and by the Department for Finance and Personnel Northern Ireland (DFPNI) to around 9,000 businesses in Northern Ireland. Data for Northern Ireland are not available from the UK Data Archive. The ABS is the largest business survey conducted by the ONS in terms of the combined number of respondents and variables it covers (62,000 questionnaires despatched in Great Britain, with around 600 different questions asked). It is the key resource for understanding the detailed structure and performance of businesses across the UK, and is a large contributor of business information to the UK National Accounts. ABS provides a number of high-level indicators of economic activity such as the total value of sales and work completed by businesses, the value of purchases of goods, materials and services, stocks, capital expenditure, and total employment costs. The contribution of different industries to the overall value of economic activity can be assessed and, although estimates of employment from each company are not now collected at the same time, by combining ABS with employment information from the Business Register and Employment Survey (BRES) it is also possible to get a measure of value added and costs per head to allow better comparison between industrial sectors of different sizes. The indicators in the ABS publications are collected and presented as monetary values or counts, for example, approximate Gross Value Added (aGVA), numbers of enterprises. They are essentially a snapshot of UK business activity, and can be used to understand the level of the contributions to the UK economy from different sectors of the economy at any one time. The ABS replaced the Annual Business Inquiry, Part 2 (ABI/2) in 2009, although ABS data are available for 2008. The BRES replaced the Annual Business Inquiry, Part 1 (ABI/1) in 2009. The BRES data for 2009 onwards are held separately under UK Data Archive SN 7463. ABI/1 and ABI/2 data for 2008 and earlier are held under UK Data Archive SN 6644, Annual Respondents Database: Secure Access (ARD). Researchers who are applying for access to the ABS and who also require data prior to 2008 are recommended to also apply for the ARD data under SN 6644. Linking to other business studies These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available. Normal 0 false false false EN-GB X-NONE X-NONE / Style Definitions / table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} Latest edition information For the 17th edition (August 2023), data files for 2020 have been revised and provisional data and documentation for 2021 have been added.

The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable, standardized chemical structure files associated with toxicity data.

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The global database monitoring software market was valued at USD 5.4 billion in 2022 and is projected to reach USD 15.2 billion by 2030, registering a CAGR of 14.71 % for the forecast period 2023-2030. Factors Impacting on Database Monitoring Software Market

The expanding prevalence of the IT environment, as well as the increasing volume of data collected, are driving the growth of the database monitoring software industry. Ubiquitous computing devices are accessible from any device, at any time, in any location, and in any data format. Any company that has a website, a social media presence, and accepts electronic payments of any type is gathering information on its customers, user habits, web traffic, demographics, and more. Data can assist industries in determining whether certain actions, products, or services are profitable, as well as where their highest expenses may be. Databases are checked on a regular basis to guarantee they are operational. This happens both during and after normal working hours. Once the system certifies that the database is operational, it monitors resources such as the CPU and other network components. Alerts for various situations can be sent to various team members so that they can act swiftly. Furthermore, replication latency is included in this form of monitoring. The system establishes benchmarks and compares them to current data. This makes observing and investigating departures from the norm simple. As a result, the global market for database monitoring software is growing.

The Restraining Factor of Database Monitoring Software Market

The increasing number of stringent rules, laws, and regulations governing software installation and development are likely hindering market expansion. Several rules, including intellectual property acts, impede software companies' international expansion. Some governments prohibit the commercial deployment and distribution of database software tools, both open-source and paid. Furthermore, the General Data Protection Regulation (GDPR) has been legislated in Europe and has been in place since May 2018. Furthermore, according to recent studies, nearly 60% of European IT enterprises must comply with GDPR legislation This will somehow restrain the growth of the global market.

Impact of the COVID-19 pandemic on the Database Monitoring Software market

The impact of the COVID-19 epidemic on the database monitoring software market. As a result of the epidemic compelling organizations to implement remote work arrangements, demand for database monitoring software is projected to rise. Remote work frequently puts a strain on IT infrastructure, necessitating more monitoring and management of databases to maintain performance, security, and availability. In reaction to the pandemic, several firms boosted their digital transformation activities. This could have resulted in increasing investments in database monitoring software to ensure the smooth functioning of newly introduced digital systems and applications. Introduction of Database Monitoring Software Market

Database monitoring is an essential function for maintaining the database management system's performance and health. The database management system receives information from the database manager, its databases, and any associated applications to aid monitoring. Furthermore, it aids in the prediction of hardware requirements based on database usage patterns and the analysis of individual applications or SQL queries. It also aids in tracking the utilization of indexes and tables. Database monitoring software employs a multi-pronged approach to performance optimization in order to help database administrators free up time and resources. Database monitoring can also help enhance various aspects of a database structure, such as fixing hotspots and bottlenecks.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)