This dataset titled Human Resources.csv contains anonymized employee data collected for internal HR analysis and research purposes. It includes fields such as employee ID, department, gender, age, job role, and employment status. The data can be used for workforce trend analysis, HR benchmarking, diversity studies, and training models in human resource analytics.The file is provided in CSV format (3.05 MB) and adheres to general data privacy standards, with no personally identifiable information (PII).Last updated: April 11, 2025. Uploaded by Anurag Pardiash.

Dataset

This dataset was created by Sudhir Singh

Released under Data files © Original Authors

Contents

Overview: 📃

This dataset provides anonymized sample employee records commonly found in HR information systems. It includes details such as employee ID, name, job title, department, business unit, gender, ethnicity, age, hire date, and annual salary. It is ideal for educational projects, algorithm demonstrations (such as B-tree implementation), HR analytics exploration, salary-related analysis examples, and more.

Columns:

• EEID: Unique Employee Identifier
• Full Name: Sample employee names
• Job Title: Employee's role (e.g., Director, Sr. Manager)
• Department: Department affiliation (e.g., IT, Engineering)
• Business Unit: Business subdivision (e.g., Manufacturing, Specialty Products)
• Gender: Employee gender (Female or Male)
• Ethnicity: Employee ethnicity (Asian, Caucasian, Other)
• Age: Age of the employee
• Hire Date: Date the employee was hired
• Annual Salary: Annual salary in numeric format

Dataset

This dataset was created by Naditya301

Released under Apache 2.0

Contents

Overview This dataset contains 30,000 synthetic employee records, including key details like names, ages, departments, positions, salaries, and joining dates. It is designed for HR analytics, salary trend analysis, and machine learning applications.

Columns Employee_ID – Unique identifier for each employee Employee_Name – Randomly generated full name Age – Employee's age (22 to 60 years) Country – Country of employment (chosen from 10 countries) Department – Assigned department (HR, Finance, Engineering, etc.) Position – Employee's job role (Manager, Developer, Analyst, etc.) Salary – Annual salary (randomly generated between $30,000 and $150,000) Joining_Date – Employee's start date (randomly selected from the past 10 years)

Use Cases HR analytics – Analyze workforce demographics and department distributions Salary trend analysis – Study compensation patterns across roles and regions Employee attrition prediction – Build machine learning models for retention insights Workforce planning – Simulate hiring and salary forecasting scenarios

This dataset is entirely synthetic and is meant for educational, research, and analytical purposes.

A. SUMMARY The San Francisco Controller's Office maintains a database of the salary and benefits paid to City employees since fiscal year 2013. B. HOW THE DATASET IS CREATED This data is summarized and presented on the Employee Compensation report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. C. UPDATE PROCESS New data is added on a bi-annual basis when available for each fiscal and calendar year. D. HOW TO USE THIS DATASET Before using please first review the following two resources: Data Dictionary - Can be found in 'About this dataset' section after click 'Show More' Employee Compensation FAQ

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

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

A. SUMMARY The San Francisco Controller's Office maintains a database of the salary and benefits paid to City employees since fiscal year 2013.

B. HOW THE DATASET IS CREATED This data is summarized and presented on the Employee Compensation report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format.

C. UPDATE PROCESS New data is added on a bi-annual basis when available for each fiscal and calendar year.

D. HOW TO USE THIS DATASET Before using please first review the following two resources: Data Dictionary - Can be found in 'About this dataset' section after click 'Show More' Employee Compensation FAQ - https://support.datasf.org/help/employee-compensation-faq

Researchers examine data about the Saudi private sector to better understand the causes and consequences of employee satisfaction and turnover. The initial step to collect this data was administering an online survey to 1,200 employees of various Saudi Arabian companies. The dataset's question axes were described using a total of 34 qualities, which were informed by insights from the literature and earlier studies on employee turnover prediction. The dataset includes three CSV files. The first file, “Original Dataset of Employee Attrition,” contains the original data before preprocessing and coding. The second file, “Employee attrition dataset for tree-based models,” contains the data after preprocessing, coding, and being oriented to tree-based models. The third file, “Employee attrition dataset for non-tree-based models,” contains the data after preprocessing, coding, and oriented to non-tree models, where categorical values are converted to numeric form and multi-valued variables are expanded into separate columns. In addition, the dataset includes two PDF files. The first file, “Online Survey Questions,” contains the questions from the data collection online survey. The second PDF file provides keys and descriptions for the nominal, ordinal, and numerical encoding in the dataset, along with the response possibilities for each variable. The questionnaire was created in Arabic and English to accommodate workers in Saudi Arabia's private sector. It includes 34 closed-ended questions. Also includes a binary target variable (attrition).

The San Francisco Controller's Office maintains a database of the salary and benefits paid to City employees since fiscal year 2013. This data is summarized and presented on the Employee Compensation report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. New data is added on a bi-annual basis when available for each fiscal and calendar year.

The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

Description

This publication presents statistics on apprenticeship starts and achievements by the industry characteristics of their employer within the 2021/22 academic year, providing further breakdowns of existing data.

Coverage

It complements the headline Apprenticeship & Traineeship 2021/22 official statistics (published in November 2022), providing detailed information on the size and industry sector of the apprenticeship employers for that year.

Data from the Individualised Learner Record (ILR) and Office for National Statistics Inter-departmental Business Register (IDBR) have been matched for the 2017/18 to 2021/22 academic years.

File formats and conventions

The underlying data files are provided in comma separated value (csv) format.

Whole numbers are rounded to the nearest 10, percentages are provided to the nearest whole number. Figures have been suppressed with the value ‘c’ for small values.

Employer-year panel dataset derived from the VRscores 2024 release. Each row corresponds to a VRscores employer identifier (VRID) and calendar year. Variables include raw and imputed counts of matched workers by voter registration party (Democratic, Republican, Other), two-party margins, partisan diversity indices, and employer name. A restricted crosswalk maintained outside this release links VRIDs to original RCIDs and supplements them with ultimate parent metadata, GVKEYs, and modal location fields for approved users. The dataset is derived from a matching of employment records from Revelio Labs (April 2025) with voter registration data from the L2 voter file (November 2024) using ensemble linkage techniques. Small employers (<5 matched workers) are excluded, one-to-one matches are enforced, and the final release contains 6,258,838 employer-year rows covering roughly 534,000 employers and 24.5 million matched workers. See the VRscores methodology and working paper for details on data processing and definitions. If you use this dataset, please cite Kagan, Max; Frake, Justin; Hurst, Reuben (2025). "VRscores: A New Measure and Dataset of Workforce Politics Using Voter Registrations" (SSRN 5104795) and Hurst, Reuben; Baskaran, Vishnu; Frake, Justin (2024). "Revelio’s Workforce Partisanship Benchmark" (SSRN 4639165). These files are provided in CSV format; they mirror the parquet release but are stored as comma-separated values for convenience.

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.

The 2022 APS Employee Census was administered to all available Australian Public Service (APS) employees, running from 9 May to 10 June 2022. \r \r The Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The Census' content is designed to establish the views of APS employees on workplace issues such as leadership, employee wellbeing, and job satisfaction.\r \r Overall, 120,662 APS employees responded to the Employee Census in 2022, a response rate of 83%.\r \r Please be aware that the very large number of respondents to the employee census means these files are over 200MB in size. Downloading and opening these files may take some time.\r \r TECHNICAL NOTES \r \r Three files are available for download.\r \r * 2022 APS Employee Census - Questionnaire: This contains the 2022 APS Employee Census questionnaire.\r \r * 2022 APS Employee Census - 5 point dataset.csv: This file contains individual responses to the 2022 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.\r \r * 2022 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2022 APS Employee Census for use with the SPSS software package. \r \r To protect the privacy and confidentiality of respondents to the 2022 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.\r \r Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. \r \r A recommended short citation is: 2022 APS Employee Census data, Australian Public Service Commission. \r \r Any queries can be directed to research@apsc.gov.au.\r

Salaries of City of Milwaukee employees by year.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Note: Due to a system migration, this data will cease to update on March 14th, 2023. The current projection is to restart the updates on or around July 17th, 2024.LMPD employee characteristic data including Race, Gender, Current Age, Date Hired, Education Level, Job Title and Assigned Division.LMPD characteristics CSV file is updated on daily frequency.Data Dictionary:AOC_CODE - the badge number of the employeeRANK_TITLE - the sworn rank of the employeeOFFICER_SEX - the gender of the employeeOFFICER_RACE - the race of the employeeOFFICER_AGE_RANGE - the range of ages that the employee's current age falls intoOFFICER_DIVISION - the division where the employee is currently assignedOFFICER_ASSIGNMENT - the employee's job assignment in the divisionOFFICER_YEARS_SWORN - the number of years that the employee has been a sworn employee

The data mainly includes the various factors affecting the departure of employees, such as salary, business trips, satisfaction with the working environment, work commitment, whether overtime work, whether promotion, and the percentage of salary increase. As well as whether the employee has left the corresponding records.

The data are divided into training data and test data, which are saved in the files train.csv and test_noLabel.csv, respectively.

The test data consists of 350 records, which is different from the training data in that the test data does not include the record of whether the employee has left the job or not. The trainee needs to use the model built from the training data as well as the given test data to come up with the prediction of whether the employee has left the job or not according to the test data.

This data shows count of employees by 100 regions within Australia over 12 years (2002 - 2014). In 2002, there were 8.5M workers, rising to 10.3M in 2014. Maps show percent change in count of employees over preceding four years eg 2010-2014.Parent data - Employee $ DATA by detailed occupation, by location (SA4), by year; #Changelog:v4.2 - added link to full database of underlying data now added to figshare (m9.figshare.4522895)v4.1 - add index.html, background20-12-16.pdf - Nectar.org.au archive of site at http://118.138.240.130; amended data totals to include AU GDP per ABSv4 - add description of SQL to extract published data from parent DB.v1 - 3. Minor edits.#descriptionThis dataset is an aggregation of all Australian Salaries and Wages by location and over 12 years in four year snapshots (2002, 2006, 2010, 2014). Some data excluded which was not allocated to a SA4 location. Source Data from ATO; Australian Tax Office.#file_descriptionHeadcountRaw.csv provides total data (employee count). Includes total counts per SA4 location, and percent change between each of the years; 2002 - 6; 2006 - 10; 2010 - 14 eg 101 means 1% increase. This file also contains the SQL query to extract this file from the parent DB.HeadcountRaw_display.csv provides data (employee count) to visualise at (1) National Map.gov.au or (2) Aurin.org.au. This only includes the data for SA4 regions which can be visualised. See #datavis below for explanation of image files.#MethodParent DB CSV files loaded into MariaDB on Nectar Infrastructure (refer NCRIS). SQL to extract this subset of data from parent DB is included in the header of HeadcountRaw.csv. Access through http://areff2000.net16.net.#sourcedataATO Data request at: data.gov.au IdeascaleOriginal data (parent data) at: data.gov.auParent data description: "Individuals data for 2001-02, 2005-06, 2009-10 and 2013-14 income years. Table 1: Salary and Wages income, by Occupation and SA4 location Table 2: Sole trader business income, by Industry and SA4 location." Sole trader data not included in this sub-collection.#current_analysisSee analysis in progress for:=> Individual income by occupation / location at: http://areff2000.net16.net (offline) or http://118.138.240.130 (Updated: 11.11.16) #datavis -How To To view on National Map (data.gov.au mapping tool). 1. Save data as csv. Data (loaded here), currently at: http://118.138.240.130/sa4_deltaHeadcountRaw_display.csv2. Open http://nationalmap.gov.au. 3. Click 'Add Data'. 4. Drag csv file onto map. 5. Click Done. 6. Select Year in control panel (lower left of screen). Raw shows count of jobs. Year shows % change from four years earlier. 7. Click on region (SA4) to see data for that region.#Data_formatYear | Occupation | Location (SA4) | Count of Workers | $ of Workers * Year: [2002, 2006, 2010, 2014]* Salary and Wages; 200,000 lines (summary only included here)* Sole Proprietors; 100,000 lines (not included here)* Occupation: Description at Australian Bureau of Statistics. (3,216 lines) (link below)* SA4 Location descriptions at: http://stat.abs.gov.au/itt/r.jsp?databyregion#/. SA4 definition/description at: http://www.abs.gov.au/ausstats/abs@.nsf/0/B01A5912123E8D2BCA257801000C64F2?opendocument #dataTotals - Salary and WagesYearWorkers (M)Earnings ($B)GDP USD($B)20028.528540020069.4372746201010.24811142201410.35841450Table 1: Aust. Salary and Wages 2002 - 2014.GDP info from: Trading Economics (link below).#datavis1. Three Chloro images made at aurin.org.au (AU researcher login required). eg Chloro12_2014 is 12 colour chloropeth, for 2010 - 2014, Chloro12_2010 is 2006 - 2010, Chloro12_2006 is 2002 - 2006.Please cite images as: Ferrers, R., ATO - User uploaded data (2016) visualised in AURIN portal (map visualisation chloropeth) on 25.8.2016. Viewed online at: https://dx.doi.org/10.6084/m9.figshare.4056282.v22. Red/Orange (year.tiff) images made at nationalmap.org.au (NM), where 2014.tiff is percent difference 2010 - 2014, 2010.tiff is 2006 - 2010, 2006.tiff is 2002 - 2006. Three scale files explain colours on each year.tiff, where related scale is [year]NM_scale.tiff.#usageThis #datavis was used in a University of Melbourne Library Hackathon - Hack for Good (25.8.16) - https://twitter.com/ValueMgmt/status/769041449862168577Slides attached below: (see Canva link; Ferrers, Li, Kreunen and Lindsay (2016). L^2 Local Livability Index. Online at: https://www.canva.com/design/DAB8-48tlEw/view)https://twitter.com/ValueMgmt/status/770144651953135616