The total number and percentage of private enterprises owned by men or women, by age group of primary owner and enterprise size.

Data on enterprise births, deaths, active enterprises and survival rates across boroughs.

Data includes:

1. the most recent annual figures for enterprise births and deaths
2. a time series of the number of births and deaths of entrprises together with a percentage of births and deaths to active enterprises in a given year
3. a time series of the number of active enterprises.
4. survival rates of enterprises for up to 5 years after birth

Notes and definitions:

• The starting point for business demography is the concept of a population of active businesses in a reference year (t). These are defined as businesses that had either turnover or employment at any time during the reference period.
• A birth is identified as a business that was present in year t, but did not exist in year t-1 or t-2. Births are identified by making comparison of annual active population files and identifying those present in the latest file, but not the two previous ones.
• A death is defined as a business that was on the active file in year t, but was no longer present in the active file in t+1 and t+2. In order to provide an early estimate of deaths, an adjustment has been made to the 2007 and 2008 deaths to allow for reactivations. These figures are provisional and subject to revision.

Data on size of firms (micro-business, SME, large) for business and employees in London by industry can be found on the ONS website.

More Business Demographics data on the ONS website

SharkTank dataset of USA/American business reality television series. Currently, the data set has information from SharkTank season 1 to Shark Tank US season 16. The dataset has 53 fields/columns and 1440+ records.

Below are the features/fields in the dataset:

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Website of startup/company
• Pitchers Gender - Gender of pitchers
• Pitchers City - US city of pitchers
• Pitchers State - US state or country of pitchers, two letter shortcut
• Pitchers Average Age - Average age of all pitchers, <30 young, 30-50 middle, >50 old
• Entrepreneur Names - Pitcher names
• Multiple Entrepreneurs - Multiple entrepreneurs are present ? 1-yes, 0-no
• US Viewership - Viewership in US, TRP rating, in millions
• Original Ask Amount - Original Ask Amount, in USD
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in USD
• Got Deal - Got the deal or not, 1-yes, 0-no
• Total Deal Amount - Total Deal Amount, in USD, including debt/loan amount
• Total Deal Equity - Total Deal Equity, in percentages
• Deal Valuation - Deal Valuation, in USD
• Number of sharks in deal - Number of sharks in deal
• Investment Amount Per Shark - Investment Amount Per Shark
• Equity Per Shark - Equity received by each Shark
• Royalty Deal - Is it royalty deal or not (1-yes)
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Loan - Loan/debt (line of credit) amount given by sharks, in USD
• Deal has conditions - Deal has conditions or not? (yes or no)
• Barbara Corcoran Investment Amount - Amount Invested by Barbara Corcoran
• Barbara Corcoran Investment Equity - Equity received by Barbara Corcoran
• Mark Cuban Investment Amount - Amount Invested by Mark Cuban
• Mark Cuban Investment Equity - Equity received by Mark Cuban
• Lori Greiner Investment Amount - Amount Invested by Lori Greiner
• Lori Greiner Investment Equity - Equity received by Lori Greiner
• Robert Herjavec Investment Amount - Amount Invested by Robert Herjavec
• Robert Herjavec Investment Equity - Equity received by Robert Herjavec
• Daymond John Investment Amount - Amount Invested by Daymond John
• Daymond John Investment Equity - Equity received by Daymond John
• Kevin O Leary Investment Amount - Amount Invested by Kevin O'Leary
• Kevin O Leary Investment Equity - Equity received by Kevin O'Leary
• Guest Investment Amount - Amount Invested by Guests
• Guest Investment Equity - Equity received by Guests
• Guest Name - Name of Guest shark, if invested in deal
• Barbara Corcoran Present - Whether Barbara Corcoran present in episode or not
• Mark Cuban Present - Whether Mark Cuban present in episode or not
• Lori Greiner Present - Whether Lori Greiner present in episode or not
• Robert Herjavec Present - Whether Robert Herjavec present in episode or not
• Daymond John Present - Whether Daymond John present in episode or not
• Kevin O Leary Present - Whether Kevin O Leary present in episode or not
• Guest Present - Whether Guest present in episode or not

This SBO dataset explores women-owned businesses and their receipts in the USA from 1997-2002. The Survey of Business owners (SBO) is a consolidation of two prior surveys, the Surveys of Minority- and Women-Owned Business Enterprises (SMOBE/SWOBE), and includes questions from a survey discontinued in 1992 on Characteristics of Business Owners (CBO).

This dataset displays the state level employer firms and employment by firm size. Data is available for each US state. The figures include the total emplers and employees, as well as figures on the firms size. Notes: For state data, a firm is as an aggregation of all establishments (locations with payroll in any quarter) owned by a parent company within a state (start-ups after March, closures before March, and seasonal firms could have zero employment). See www.sba.gov/advo/research/data.html for more detail. Source: U.S. Small Business Administration, Office of Advocacy, based on data provided by the U.S. Census Bureau.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Abstract

The study was conducted in Serbia between October 2008 and February 2009 as part of the first round of The Management, Organization and Innovation Survey. Data from 135 manufacturing companies with 50 to 5,000 full-time employees was analyzed.

The survey topics include detailed information about a company and its management practices - production performance indicators, production target, ways employees are promoted/dealt with when underperforming. The study also focuses on organizational matters, innovation, spending on research and development, production outsourcing to other countries, competition, and workforce composition.

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment is defined as a separate production unit, regardless of whether or not it has its own financial statements separate from those of the firm, and whether it has it own management and control over payroll. So the bottling plant of a brewery would be counted as an establishment.

Universe

The survey universe was defined as manufacturing establishments with at least fifty, but less than 5,000, full-time employees.

Kind of data

Sample survey data [ssd]

Sampling procedure

Random sampling was used in the study. For all MOI countries, except Russia, there was a requirement that all regions must be covered and that the percentage of the sample in each region was required to be equal to at least one half of the percentage of the sample frame population in each region.

In most countries the sample frame used was an extract from the Orbis database of Bureau van Dijk, which was provided to the Consultant by the EBRD. The sample frame contained details of company names, location, company size (number of employees), company performance measures and contact details. The sample frame downloaded from Orbis was cleaned by the EBRD through the addition of regional variables, updating addresses and phone numbers of companies.

Examination of the Orbis sample frames showed their geographic distributions to be wide with many locations, a large number of which had only a small number of records. Each establishment was selected with two substitutes that can be used if it proves impossible to conduct an interview at the first establishment. In practice selection was confined to locations with the most records in the sample frame, so the sample frame was filtered to just the cities with the most establishments.

The quality of the frame was assessed at the onset of the project. The frame proved to be useful though it showed positive rates of non-eligibility, repetition, non-existent units, etc. These problems are typical of establishment surveys. For Serbia, the percentage of confirmed non-eligible units as a proportion of the total number of contacts to complete the survey was 26.7% (82 out of 307 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

Two different versions of the questionnaire were used. Questionnaire A was used when interviewing establishments that are part of multiestablishment firms, while Questionnaire B was used when interviewing single-establishment firms. Questionnaire A incorporates all questions from Questionnaire B, the only difference is in the reference point, which is the so-called national firm in the first part of Questionnaire A and firm in Questionnaire B. Second part of the questionnaire refers to the interviewed establishment only in both Questionnaire A and Questionnaire B. Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Item non-response was addressed by two strategies: - For sensitive questions that may generate negative reactions from the respondent, such as ownership information, enumerators were instructed to collect the refusal to respond as (-8). - Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

Survey non-response was addressed by maximising efforts to contact establishments that were initially selected for interviews. Up to 15 attempts (but at least 4 attempts) were made to contact an establishment for interview at different times/days of the week before a replacement establishment (with similar characteristics) was suggested for interview. Survey non-response did occur, but substitutions were made in order to potentially achieve the goals.

Additional information about sampling, response rates and survey implementation can be found in "MOI Survey Report on Methodology and Observations 2009" in "Technical Documents" folder.

(StatCan Product) Annual business entries per 10,000 people and the percentage of firms considered high growth by employee definition for selected provinces. Customization details: This information product has been customized to present information on annual business entries per 10,000 people and the percentage of firms considered high growth by employee definition for BC, AB, SK, MB, ON and QC for 2001 to 2010. For the annual business entries per 10,000 people, NAICS 9111-9191 were exclued. Percentage of firms considered high growth by employee definition: High Growth enterprises measured in employment refer to all enterprises with average annualised growth in employees greater than 20% per annum, over a three year period, and with 10 or more employees in the beginning of the observation period. % measure is number of high-growth enterprises as a percentage of the population of enterprises with ten or more employees. Two tables have been presented for % high growth - one that treats the denominator as being all firms that have more than 10 Average Labour Units (ALUs) in the starting year, and the other that treats the denominator as being all firms that have more than 10 ALUs in the starting year and are active in the final year. The figures do not change very much as a result of this, and are included for completeness. An ALU is described as follows: In Canada, employing businesses are required to register with Canada Revenue Agency using the Business Number and issue to each of their employees a T4 slip that summarizes earnings received in the year. This process creates a link between the employee and the business through the Business Number. This link is the backbone of LEAP, and the reported payroll allows estimates of annual employment to be made. The payroll is converted to employment (called ALUs or Average Labour Units) using conversion factors derived from the Survey of Employment, Payroll and Hours (SEPH). The Average Labour Unit (ALU) is a calculated measure portraying the average employment represented by a business's annual payroll if it paid the average earnings typical in its particular 4-digit NAICS industry, province and business size category. The ALU is calculated by converting each business's annual payroll into an approximation of the annual average level of employment it represented. The ALU employment estimate is derived by dividing the business's annual payroll (from T4 system) by the corresponding industry/province/size class average annual earnings per employee (from SEPH system). The link to this information can be found: http://www23.statcan.gc.ca:81/imdb/p2SV.pl?Function=getSurvey&SDDS=8013&lang=en&db=imdb&adm=8&dis=2

Content

The hospital readmission rate PUF presents nation-wide information about inpatient hospital stays that occurred within 30 days of a previous inpatient hospital stay (readmissions) for Medicare fee-for-service beneficiaries. The readmission rate equals the number of inpatient hospital stays classified as readmissions divided by the number of index stays for a given month. Index stays include all inpatient hospital stays except those where the primary diagnosis was cancer treatment or rehabilitation. Readmissions include stays where a beneficiary was admitted as an inpatient within 30 days of the discharge date following a previous index stay, except cases where a stay is considered always planned or potentially planned. Planned readmissions include admissions for organ transplant surgery, maintenance chemotherapy/immunotherapy, and rehabilitation.

This dataset has several limitations. Readmissions rates are unadjusted for age, health status or other factors. In addition, this dataset reports data for some months where claims are not yet final. Data published for the most recent six months is preliminary and subject to change. Final data will be published as they become available, although the difference between preliminary and final readmission rates for a given month is likely to be less than 0.1 percentage point.

Data Source: The primary data source for these data is the CMS Chronic Condition Data Warehouse (CCW), a database with 100% of Medicare enrollment and fee-for-service claims data. For complete information regarding data in the CCW, visit http://ccwdata.org/index.php. Study Population: Medicare fee-for-service beneficiaries with inpatient hospital stays.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Justyn Warner on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under NA

Information on farm household income and farm household composition. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Household Income and Household Composition, England If you require the datasets in a more accessible format, please contact fbs.queries@defra.gsi.gov.uk Background and guidance on the statistics Information on farm household income and farm household composition was collected in the Farm Business Survey (FBS) for England for the first time in 2004/05. Collection of household income data is restricted to the household of the principal farmer from each farm business. For practical reasons, data is not collected for the households of any other farmers and partners. Two-thirds of farm businesses have an input only from the principal farmer’s household (see table 5). However, details of household composition are collected for the households of all farmers and partners in the business, but not employed farm workers. Data on the income of farm households is used in conjunction with other economic information for the agricultural sector (e.g. farm business income) to help inform policy decisions and to help monitor and evaluate current policies relating to agriculture in the United Kingdom by Government. It also informs wider research into the economic performance of the agricultural industry. This release gives the main results from the income and composition of farm households and the off-farm activities of the farmer and their spouse (Including common law partners) sections of the FBS. These sections include information on the household income of the principal farmer’s household, off-farm income sources for the farmer and spouse and incomes of other members of their household and the number of working age and pensionable adults and children in each of the households on the farm (the information on household composition can be found in Appendix B). This release provides the main results from the 2013/14 FBS. The results are presented together with confidence intervals. Survey content and methodology The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2013 there were just over 58 thousand farm businesses meeting this criteria. Since 2009/10 a sub-sample of around 1,000 farms in the FBS has taken part in both the additional surveys on the income and composition of farm households and the off-farm activities of the farmer and their spouse. In previous years, the sub-sample had included over 1,600 farms. As such, caution should be taken when comparing to earlier years. The farms that responded to the additional survey on household incomes and off-farm activities of the farmer and spouse had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. However, there is a smaller proportion of very large farms in the additional survey than in the main FBS. Full details of the characteristic of responding farms can be found at Appendix A of the notice. For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey Data analysis The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the additional survey on household incomes and off-farm activities of the farmer and spouse was voluntary and a sample of around 1,000 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income). Accuracy and reliability of the results We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval. The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. For the Farm Business Survey, the confidence limits shown are appropriate for comparing groups within the same year only; they should not be used for comparing with previous years since they do not allow for the fact that many of the same farms will have contributed to the Farm Business Survey in both years. Availability of results This release contains headline results for each section. The full set of results can be found at: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications Defra statistical notices can be viewed on the on the statistics pages of the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates. Data Uses Data from the Farm Business Survey (FBS) are provided to the EU as part of the Farm Accountancy Data Network (FADN). The data have been used to help inform policy decisions (e.g. Reform of Pillar 1 and Pillar 2 of Common Agricultural Policy) and to help monitor and evaluate current policies relating to agriculture in England (and the EU). It is also widely used by the industry for benchmarking and informs wider research into the economic performance of the agricultural industry. User engagement As part of our ongoing commitment to compliance with the Code of Practice for Official Statistics http://www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html, we wish to strengthen our engagement with users of these statistics and better understand the use made of them and the types of decisions that they inform. Consequently, we invite users to make themselves known, to advise us of the use they do, or might, make of these statistics, and what their wishes are in terms of engagement. Feedback on this notice and enquiries about these statistics are also welcome. Definitions Household income of the principal farmer Principal farmer’s household income has the following components: (1) The share of farm business income (FBI) (including income from farm diversification) attributable to the principal farmer and their spouse. (2) Principal farmer’s and spouse’s off farm income from employment and self-employment, investment income, pensions and social payments. (3) Income of other household members. The share of farm business income and all employment and self-employment incomes, investment income and pension income are recorded as gross of income tax payments and National Insurance contributions, but after pension contributions. In addition, no deduction is made for council tax. Household A household is defined as a single person or group of people living at the same address as their only or main residence, who either share one meal a day together or share the living accommodation. A household must contain at least one person who received drawings from the farm business or who took a share of the profit from the business. Drawings Drawings represent the monies which the farmer takes from the business for their own personal use. The percentage of total drawings going to each household is collected and is used to calculate the total share of farm business income for the principal farmer’s household. Mean Mean household income of individuals is the ”average”, found by adding up the weighted household incomes for each individual farm in the population for analysis and dividing the result by the corresponding weighted number of farms. In this report average is usually taken to refer to the mean. Percentiles These are the values which divide the population for analysis, when ranked by an output variable (e.g. household income or net worth), into 100 equal-sized groups. E.g. twenty five per cent of the population would have incomes below the 25th percentile. Median Median household income divides the population, when ranked by an output variable, into two equal sized groups. The median of the whole population is the same as the 50th percentile. The term is also used for the midpoint of the subsets of the income distribution Quartiles Quartiles are values which divide the population, when ranked by an output variable, into four equal-sized groups. The lowest quartile is the same as the 25th percentile. The divisions of a population split by quartiles are referred to as quarters in this publication. Quintiles Quintiles are values which divide the population, when ranked by an output variable, into five equal-sized groups. The divisions of a population split by quintiles are referred to as fifths in this publication. Assets Assets include milk and livestock quotas, as well as land, buildings (including the farm house), breeding livestock, and machinery and equipment. For tenanted farmers,

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

The dataset supports measure S.D.4.a of SD23. The Austin Municipal Court offers services via in person, phone, mail, email, online, in the community, in multiple locations, and during non-traditional hours to make it easier and more convenient for individuals to handle court business. This measure tracks the percentage of customers that utilize court services outside of normal business hours, defined as 8am-5pm Monday-Friday, and how many payments were made by methods other than in person. This measure helps determine how Court services are being used and enables the Court to allocate its resources to best meet the needs of the public. Historically, almost 30% of the operational hours are outside of traditional hours and the average percentage of payments made by mail and online has been over 59%. View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/c7z3-geii Data source: electronic case management system and manual tracking of payments received via mail. Calculation: Business hours are manually calculated annually. - A query is run from the court’s case management system to calculate how many monetary transactions were posted. S.D.4.a: Numerator: Number of payments received by mail is entered manually by the Customer Service unit that processes all incoming mail. S.D.4.a Denominator: Total number of web payments is calculated using a query to calculate a total number of payments with a payment type ‘web’ in the case management system. Measure time period: Annual (Fiscal Year) Automated: No Date of last description update: 4/10/2020

Created with a 500 meter side hexagon grid, we undertook a regression analysis creating a correlation matrix utilising a number of demographic indicators from the Local Insight OCSI platform. This dataset is showing the distribution of metrics that were found to have the strongest relationships, with the base comparison metric of At risk employees (as a result of COVID-19) by employee residence. This dataset contains the following metrics:At risk employees (as a result of COVID-19) by employee residence - Shows the proportion of employees that are at risk of losing their jobs following the outbreak of COVID-19 - calculated based on the latest furloughing data from the ONS and the employee profile for each local authority. The data is derived from Wave 2 of the ONS Business Impact of Coronavirus Survey (BICS) which contains data on the furloughing of workers across UK businesses between March 23 to April 5, 2020 see https://www.ons.gov.uk/generator?uri=/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/furloughingofworkersacrossukbusinesses/23march2020to5april2020/574ca854&format=csv for details. This data includes responses from businesses that were either still trading or had temporarily paused trading. This has been mapped against the industrial composition of employee jobs at OA, LSOA, MSOA and Local Authority level to estimate which are most exposed to labour market risks associated with the Covid-19. The industrial composition of employee jobs is based on the employee place of residence rather than where they work. The data on the industrial composition of local areas comes from the 2011 Census Industrial classification, which is publicly accessible via NOMIS. The methodology is adapted from the RSA at-risk Local Authorities publication - https://www.thersa.org/about-us/media/2020/one-in-three-jobs-in-parts-of-britain-at-risk-due-to-covid-19-local-data-reveals This approach calculates the total number of employees at risk in each local area by identifying the number of employees in each industry in that area (based on employee residence) multiplied by the estimated percentage of those that have been furloughed on the Government's Coronavirus Job Retention Scheme (CJRS). The CRJS was set up by the Government specifically to prevent growing unemployment and the National Institute for Economic and Social Research (NIESR) has described furloughed workers as technically unemployed. It therefore looks to be the best available data with which to calculate medium-term employment risk as a result of Covid-19. This is then divided by the total number of employees in each local area (by place of residence) to calculate the percentage of employees at risk of losing their jobs. Note, employees in industry sectors which were not recorded in the ONS Business Impact of Coronavirus Survey (BICS) due to inadequate sample size have not been included in the numerator or denominator for this dataset - these include Agriculture, forestry and fishing, Mining and quarrying, Electricity, gas, steam and air conditioning supply, Financial and insurance activities, Real estate activities. Public administration and defence; compulsory social security and activities of households as employers; undifferentiated goods - and services - producing activities of households for own use. Social grade (N-SEC): 2. Lower managerial, administrative and professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 2. Lower managerial, administrative and professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Lower managerial, administrative and professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.IoD 2019 Education, Skills and Training Rank - The Indices of Deprivation (IoD) 2019 Education Skills and Training Domain measures the lack of attainment and skills in the local population. The indicators fall into two sub-domains: one relating to children and young people and one relating to adult skills. These two sub-domains are designed to reflect the 'flow' and 'stock' of educational disadvantage within an area respectively. That is the 'children and young people' sub-domain measures the attainment of qualifications and associated measures ('flow') while the 'skills' sub-domain measures the lack of qualifications in the resident working age adult population ('stock'). Children and Young People sub-domain includes: Key stage 2 attainment: The average points score/scaled score of pupils taking reading writing and mathematics Key stage 2 exams; Key stage 4 attainment: The average capped points score of pupils taking Key stage 4; Secondary school absence: The proportion of authorised and unauthorised absences from secondary school; Staying on in education post 16: The proportion of young people not staying on in school or non-advanced education above age 16 and Entry to higher education: The proportion of young people aged under 21 not entering higher education. The Adult Skills sub-domain includes: Adult skills: The proportion of working age adults with no or low qualifications women aged 25 to 59 and men aged 25 to 64; English language proficiency: The proportion of working age adults who cannot speak English or cannot speak English well women aged 25 to 59 and men aged 25 to 64. Data shows Average LSOA Rank, a lower rank indicates that an area is experiencing high levels of deprivation.Social grade (N-SEC): 1 Higher managerial, administrative and professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 1 Higher managerial, administrative and professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Higher managerial, administrative and professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Total annual household income estimate - Shows the average total annual household income estimate (unequivalised). These figures are model-based estimates, taking the regional figures from the Family Resources Survey and modelling down to neighbourhood level based on characteristics of the neighbourhood obtained from census and administrative statistics.Household is not deprived in any dimension - Shows households which are not deprived on any of the four Census 2011 deprivation dimensions. The Census 2011 has four deprivation dimension characteristics: a) Employment: Any member of the household aged 16-74 who is not a full-time student is either unemployed or permanently sick; b) Education: No member of the household aged 16 to pensionable age has at least 5 GCSEs (grade A-C) or equivalent AND no member of the household aged 16-18 is in full-time education c) Health and disability: Any member of the household has general health 'not good' in the year before Census or has a limiting long term illness d) Housing: The household's accommodation is either overcrowded; OR is in a shared dwelling OR does not have sole use of bath/shower and toilet OR has no central heating. These figures are taken from responses to various questions in census 2011. Rate calculated as = (Household is not deprived in any dimension (census QS119))/(All households (census QS119))*100.Occupation group: Professional occupations - Shows the proportion of people in employment (aged 16-74) working in the Occupation group: Professional occupations. An individual's occupation group is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Professional occupations (census KS608))/(All usual residents aged 16 to 74 in employment the week before the census (census KS608))*100.Social grade (N-SEC): 1.2 Higher professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 1.2 Higher professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Higher professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Sport England Market Segmentation: Competitive Male Urbanites - proportion of people living in the area that are classified as Competitive Male Urbanites in the Sports Market Segmentation.Net annual household income estimate after housing costs - Shows the average annual household income estimate (equivalised to take into account variations in household size) after housing costs are taken into account. These figures are model-based estimates, taking the regional figures from the Family Resources Survey and modelling down to neighbourhood level based on characteristics of the neighbourhood obtained from census and administrative statistics.

Cyclistic ride share data cleaned in Excel with following steps:

• Aligned Data in centre between the cells
• Sorted By start date
• First row bolded and changed to proper case for readablity
• Added new column - ride length by using started at and ended at columns, D-C
• Added new column - day of the week using Started_At column C (1-sunday, 7-saturday) -Deleted rows where started at < Ended at as gives negative ride length, the number of rouge rows are not significant, hence deleted.

Data License Agreement Lyft Bikes and Scooters, LLC (“Bikeshare”) operates the City of Chicago’s (“City”) Divvy bicycle sharing service. Bikeshare and the City are committed to supporting bicycling as an alternative transportation option. As part of that commitment, the City permits Bikeshare to make certain Divvy system data owned by the City (“Data”) available to the public, subject to the terms and conditions of this License Agreement (“Agreement”). By accessing or using any of the Data, you agree to all of the terms and conditions of this Agreement.

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Abstract copyright UK Data Service and data collection copyright owner. The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete. While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451). The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data. The ASHE improves on the NES in the following ways:the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the dataweightings have been introduced to take account of the population size (significant biases were a known problem in NES data)the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NESLinking to other business studies These data contain Inter-Departmental Business Register (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. Observations from Northern Ireland The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey. Local unit reference variable, luref The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.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.Latest Edition InformationFor the twenty-fifth edition (April 2024), the data file 'ashegb_2022r_2023p_soc20_ restricted' has been updated, along with the accompanying data dictionary. An error was identified with the previous edition data file. The work postcode was not included for around 1,000 records (across the board) of the 148,000 records in the 2022 sample. This would have a minimal impact on high level analysis, but affect detailed geography level analysis. The 2022 published tables were not affected. Main Topics: The ASHE contains a small number of variables for each individual, relating to wages, hours of work, pension arrangements, and occupation and industrial classifications. There are also variables for age, gender and full/part-time status. Because the data are collected by the employer, there are also variables relating to the organisation employing the individual. These include employment size and legal status (e.g. public company). Various geography variables are included in the data files. Simple random sample

The 2007 World Bank Group Entrepreneurship Survey measures entrepreneurial activity in 84 developing and industrial countries over the period 2003-2005. The database includes cross-country, time-series data on the number of total and newly registered businesses, collected directly from Registrar of Companies around the world. In its second year, this survey incorporates improvements in methodology, and expanded participation from countries covered, allowing for greater cross-border compatibility of data compared with the 2006 survey. This joint effort by the IFC SME Department and the World Bank Developing Research Group is the most comprehensive dataset on cross-country firm entry data available today. This database The World Bank Group Entrepreneurship Dataaset presents data collected primarily from country business registries using the first annual World Bank Group Questionnaire on Entrepreneurship (alternative sources were tax authorities, finance ministries, and national statistics offices). For more information on the author of the database, Leora Klapper, visit: http://go.worldbank.org/DK5AHCQSO0. This data was access at the preceeding link, on October 11, 2007. Please visit the link for more information in regards to this dataset.

Dataset Description

The dataset includes 1,000 records with information about loan applications, including variables related to the applicant's financial status, credit history, and loan details. The goal is to analyze patterns in credit risk or build models to predict loan defaults.

Columns:

• checking_balance: Customer's current account balance in deutschmarks, classified as < 0 DM (negative balance), 1 - 200 DM, > 200 DM, or unknown (unknown).
• months_loan_duration: Duration of the loan in months.
• credit_history: Credit history of the applicant.
• purpose: Purpose of the loan.
• amount: Loan amount.
• savings_balance: Savings account balance.
• employment_duration: Length of employment.
• percent_of_income: Percentage of income allocated to loan repayment.
• years_at_residence: Years at the current residence.
• age: Applicant's age.
• other_credit: Presence of other credit agreements.
• housing: Housing status (e.g., rent, own).
• existing_loans_count: Number of existing loans.
• job: Job type or classification.
• dependents: Number of dependents.
• phone: Availability of a telephone.
• default: Target variable indicating loan default ("yes" or "no").

Inspiration

This dataset can be used for: - Building predictive models for loan default. - Exploring relationships between financial variables and credit risk. - Enhancing your understanding of credit risk analysis.

License

This dataset is published under the CC BY-NC-SA 4.0 license: - Permitted: Educational, research, and personal use. - Restricted: Commercial use is not allowed. - Attribution: Credit to Universidad de Santiago de Chile is required. - Sharing: Derivative works must use the same license.

This dataset was originally provided by the Universidad de Santiago de Chile as part of the course "Machine Learning for Management". I am not the original creator of the data, and my role is solely to share this resource for educational and research purposes. All rights to the original data belong to the university and/or the original authors.

This dataset may not be used for commercial purposes or in contexts that violate the copyright or policies of the institution that created it. Users are responsible for complying with the terms of use specified in the accompanying license and should ensure they provide appropriate credit.

Additional Notes If you are a student or researcher interested in using this dataset, please make sure to give proper credit to the original source in your publications or projects.

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (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.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

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.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.