United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men was 49.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men reached a record high of 95.00000 in January of 2000 and a record low of 46.00000 in January of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men (LEU0254609800A) from 2000 to 2024 about occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Women (LEU0254770000A) from 2000 to 2024 about second quartile, occupation, females, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over (LEU0254556400A) from 2000 to 2024 about second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over was 923.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over reached a record high of 923.00000 in January of 2024 and a record low of 437.00000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Men (LEU0254663200A) from 2000 to 2023 about second quartile, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals. NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data. NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

Explore the progression of average salaries for graduates in Business Information Technologydata Entry from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Business Information Technologydata Entry relative to other fields. This data is essential for students assessing the return on investment of their education in Business Information Technologydata Entry, providing a clear picture of financial prospects post-graduation.

During the early days of professional baseball, the dominant major leagues imposed a “reserve clause” designed to limit player wages by restricting competition for labor. Entry into the market by rival leagues challenged the incumbent monopsony cartel’s ability to restrict compensation. Using a sample of player salaries from the first 40 years of the reserve clause (1880-1919), this study examines the impact of inter-league competition on player wages. This study finds a positive salary effect associated with rival league entry that is consistent with monopsony wage suppression, but the effect is stronger during the 20th century than the 19th century. Changes in levels of market saturation and minor-league competition may explain differences in the effects between the two eras.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for business information technologydata entry in the U.S.

As of the 2023/24 academic year, graduates from the Massachusetts Institute of Technology (MIT) had a starting salary of 110,200 U.S. dollars, and a mid-career salary of 196,900 U.S. dollars. Top universities in the United States One of the top universities in the United States, Harvey Mudd College, is located in Claremont, California. Not only do graduates earn a high salaries after graduation, they also pay the most. In the academic year of 2020-2021, Harvey Mudd College was one of the most expensive school by total annual cost. The best university in the United States in 2021 belonged to the University of California, Berkeley. The Ivy League The Ivy League is a group of eight private universities in the Northeastern United States. It is not only a collegiate athletic conference, but also a group of highly respected academic institutions. They are usually regarded as the best eight universities in the United States and the world. They are extremely selective with their admissions process. However, these universities are extremely expensive to attend. Despite the high price tag, students who graduate from Princeton University have the highest early career salary out of all Ivy League attendees in 2021. This is compared to the overall expected starting salaries of recent college graduates across the United States, which was less than 35,000 U.S. dollars.

German law graduates holding a doctorate degree can currently expect the ******* average gross starting salary in the country when they enter the job market. Other degrees with good earning prospects include medicine, computer science (also with a doctorate degree), and industrial engineering. In comparison, those who studied graphics/design, humanities and social sciences are at the ****** of the starting salary food chain. Law courses among most attended Law, economics and social sciences were the subject groups seeing the ******* student numbers in German universities, totaling over *** million in 2023/2024. Engineering and mathematics rounded up the top three. German universities offer a variety of internationally recognized degrees, the Bachelor being the most frequently taken type of final exam. Slow yearly salary increase Among selected countries in the European Union, Germany ranks ***** in terms of average annual wages. All the same, when studying the change in average annual pay specifically in Germany during the last decade, a slow, but steady increase is visible year after year, until the coronavirus (COVID-19) pandemic hit in 2020. Since then, the average wage has been decreasing and in 2023 was around the same level as in 2017.

In 2022, it was estimated that the CEO-to-worker compensation ratio was 344.3 in the United States. This indicates that, on average, CEOs received more than 344 times the annual average salary of production and nonsupervisory workers in the key industry of their firm.

Abstract

A. Objectives

To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data

Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered

Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National coverage, 17 administrative regions

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 20 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2006 BLES Survey Sampling Frame (SSF 2006) is an integrated list of establishments culled from the 2004 List of Establishments of the National Statistics Office, updated 2004 BLES Sampling Frame based on the status of establishments reported in the 2003/2004 BLES Integrated Survey (BITS). Reports on closures and retrenchments of establishments submitted to the Regional Offices of the Department of Labor and Employment were also considered in preparing the 2006 frame.

Sampling design: The OWS is a sample survey of non-agricultural establishments employing 20 persons or more where the survey domain is the industry. Those establishments employing at least 200 persons are covered with certainty and the rest are sampled (stratified random sampling). The design does not consider the region as a domain to allow for more industry coverage.

Sample size: For 2006 OWS, number of establishments covered was 7,630 of which, 6,432 were eligible units.

Note: Refer to Field Operations Manual Chapter 2 Section 2.5.

Sampling deviation

Not all of the fielded questionnaires are accomplished. During data collection, there are reports of permanent closures, non-location, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements (three consecutive survey rounds for "can not be located" establishments) of the frame and their count is not considered in the estimation. Non-respondents are made up of refusals, strikes or temporary closures, can not be located (less than three consecutive survey rounds) and those establishments whose questionnaires contain inconsistent item responses and have not replied to the verification queries by the time output table generation commences.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by BLES and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2006 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2006 OWS; results of the 2004 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color.

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the BLES and DOLE Regional Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in determining the correct occupational sheet that should be furnished to the respondent.

Results of the 2004 OWS (Page 12) The results of the 2004 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Note: Refer to questionnaire and List of Monitored Occupations.

Cleaning operations

Data were manually and electronically processed. Upon collection of accomplished questionnaires, enumerators performed field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the Field Operations Manual. The forms were again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertaook the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries were returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 87.56%.

Sampling error estimates

Estimates of the sampling errors computed.

Note: Refer to Coefficients of Variation.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year.

NOTE: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2012-2016 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Questions for "wage and salary" and "tips, bonuses and commissions" were asked separately for the first time during non-response follow-up via Computer Assisted Telephone Interview (CATI) and Computer Assisted Personal Interview (CAPI). Prior to 2013 these questions were asked in combination, "wages, salary, tips, bonuses and commissions."..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2012-2016 American Community Survey 5-Year Estimates

Australia Minimum Weekly Rate of Pay: Mining Industry: Entry Level: Introductory data was reported at 768.000 AUD in 2019. This records an increase from the previous number of 745.600 AUD for 2018. Australia Minimum Weekly Rate of Pay: Mining Industry: Entry Level: Introductory data is updated yearly, averaging 672.800 AUD from Jun 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 768.000 AUD in 2019 and a record low of 591.000 AUD in 2010. Australia Minimum Weekly Rate of Pay: Mining Industry: Entry Level: Introductory data remains active status in CEIC and is reported by Fair Work Commission. The data is categorized under Global Database’s Australia – Table AU.G029: Minimum Weekly Rate of Pay: Mining Industry.

Euro Area - Labour input: Gross wages and salaries was 127.90 points in December of 2023, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Euro Area - Labour input: Gross wages and salaries - last updated from the EUROSTAT on July of 2025. Historically, Euro Area - Labour input: Gross wages and salaries reached a record high of 127.90 points in December of 2023 and a record low of 101.70 points in June of 2020.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2010, the 2010 Census provides the official counts of the population and housing units for the nation, states, counties, cities and towns. For 2006 to 2009, the Population Estimates Program provides intercensal estimates of the population for the nation, states, and counties..Explanation of Symbols:.An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2006-2010 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2006-2010 American Community Survey

This dataset provides a curated and labeled subset of employer entries derived from Wikidata, with the goal of improving the quality and usability of employer data. While Wikidata is an invaluable open resource, direct use often necessitates cleaning. This dataset addresses that need by offering metadata, statistics, and labels to help users identify and utilise valid employer information. An employer is generally defined here as a company or entity that provides employment paying wages or a salary. The dataset specifically screens out entries that do not represent true employers, such as individuals or plurals. It is particularly useful for tasks involving data cleaning, entity recognition, and understanding employment nomenclature.

Columns

• item_id: The unique Wikidata item identifier (QCode without the 'Q' prefix).
• employer_count: The number of Wikidata entries associated with this specific employer reference.
• employer: The text label of the employer's name, sourced from Kensho's English labels.
• description: The accompanying description of the Wikidata employer entry, also from Kensho.
• in_google_news: A binary indicator (0 for no, 1 for yes) showing if the occupation exists within the GoogleNews embedding.
• language_detected: A three-digit language code, identified using FastText language detection.
• source: Indicates the origin of the information, such as Wikidata or Wikipedia.
• label: A binary label (0 for invalid employer, 1 for valid employer) indicating the data's quality.
• labeled_by: Specifies the method used for labeling, including human, classifier_gnew, classifier_bert, or cleanlab.
• label_error_reason: Provides the specific reason if a label is deemed an error, such as 'domain' or 'plural'.

Distribution

This dataset is provided as a single CSV file, named employers.wikidata.all.labeled.csv. Its current version is 1.0, with a file size of approximately 5.98 MB. The dataset contains a substantial number of entries, with item_id having 60656 values, employer having 60456 values, and description having 60640 values.

Usage

This dataset is ideal for various applications, including: * Detecting new trends in employers, occupations, and employment terminology. * Automatic error correction of employer entries. * Converting plural forms of entities to singular forms. * Training Named Entity Recognition (NER) models to identify employer names. * Building Question/Answer models that can understand and respond to queries about employers. * Improving the accuracy of FastText language detection models. * Assessing FastText accuracy with limited data.

Coverage

The dataset's coverage is global, drawing data from a Wikidata dump dated 2 February 2020. It includes employer entries from various linguistic contexts, as indicated by the language_detected column, showcasing multilingual employer names and descriptions. The content primarily focuses on entities and organisations that meet the definition of an employer, rather than specific demographic groups.

License

CC BY-SA

Who Can Use It

This dataset is suitable for: * Data scientists and machine learning engineers working on natural language processing tasks. * Researchers interested in data quality, entity resolution, and knowledge graph analysis. * Developers building applications that require accurate employer information. * Anyone needing to clean and validate employer data for various analytical or operational purposes.

Dataset Name Suggestions

• Wikidata Labeled Employers
• ML-Ready Wikidata Employer Data
• Cleaned Wikidata Employer References
• Global Employer Dataset (Wikidata)
• Validated Employer Entities

Attributes

Original Data Source: ML-You-Can-Use Wikidata Employers labeled