We conducted a cross-sectional study of the publicly available 2022 Open Payments data to characterize and quantify sponsored events (available for download at: https://www.cms.gov/priorities/key-initiatives/open-payments/data/dataset-downloads). Data sources We downloaded the 2022 dataset ZIP files from the Open Payments website on June 30th, 2023. We included all records for nurse practitioners, clinical nurse specialists, certified registered nurse anesthetists, and certified nurse-midwives (hereafter advanced practiced registered nurses (APRNs)); and allopathic and osteopathic physicians (hereafter, ‘physicians’). To ensure consistency in provider classification, we linked Payments data to the National Plan and Provider Enumeration System data (June 2023) by National Provider Identifier (NPI) and the National Uniform Claim Committee (NUCC) and excluded individuals with an ambiguous provider type. Event-centric analysis of Open Payments records: Creating an event typology We included only payments classified as “food and beverage” to reliably identify distinct sponsored events. We reasoned that food and beverage would be consumed on the same day in the same place, thus assumed that records for food and beverage associated with the same event would share the date of payment and location. We also assumed that the reported value of a food and beverage payment is the total cost of the hospitality divided by the number of attendees, thus grouped payment records with the same amount, rounded to the nearest dollar. Inferring which Open Payment records relate to the same sponsored event requires analytic decisions regarding the selection and representation of variables that define an event. To understand the impact of these choices, we undertook a sensitivity analysis to explore alternative ways to group Open Payments records for food and beverage, to determine how combination of variables, including date (specific date or within the same calendar week), amount (rounded to nearest dollar), and recipient’s state, affected the identification of sponsored events in the Open Payments data set. We chose to define a sponsored event as a cluster of three or more individual payment records for food and beverage (nature of payment) with the following matching Open Payments record variables: • Submitting applicable manufacturer (name) • Product category or therapeutic area • Name of drug or biological or device or medical supply • Recipient state • Total amount of payment (USD, rounded to nearest dollar) • Date of payment (exact) After examining the distribution of the data, we classified events in terms of size (≥20 attendees as “large” and 3-<20 as “small”) and amount per person. We categorized events <$10 as “coffee”, $10-<$30 as “lunch”, $30-<$150 as “dinner”, and ≥$150 as “banquet”.

🧑‍💼 Employees' Payroll in Los Angeles 🧑‍💼

Payroll of 685,000 Employees in Los Angeles (01-2013 to 06-2022)

🔐 Dataset File 🔐

payroll.csv

❓ Description ❓

This dataset contains information about 685,000 Los Angeles's employees .

The data was extracted between January 1th 2013 and June 317th in 2022.

📝 Variables 📝

The file payroll.csv contains a huge table of Los Angeles's employees' information in the following columns:

• Record_NBR: unique number to identify an employee;
• Pay_Year: tax year employee was paid. This is not Fiscal Year;
• Department_NO: department Number in City Payroll System;
• Department_Title: title of City Department;
• Job_Class_PGrade: Job Class and Pay Grade;
• Job_Title: Job Title;
• Employment_Type: employment type - Full Time, Part Time, or Per Event;
• Job_Status: employee's job status at the time the data was uploaded;
• MOU: Memorandum of Understanding;
• MOU_Title: title of Memorandum of Understanding;
• Regular_Pay: regular work hours payment;
• Overtime_Pay: payments attributable to hours worked beyond regular work schedule;
• All_Other_Pay: any payments other than Regular and Overtime. This includes bonuses, adjustments, and lump sum payouts. Examples of bonuses include Permanent, Longevity, and Temporary Bonuses. Lump Sum Pay includes significant one-time payouts due to retirement, lawsuit settlements, or other adjustments;
• Total_Pay: sum of regular, overtime and all other payments;
• City_Retirement_Contributions: estimated payments made by the City towards employee's retirement;
• Benefit_Pay: city contribution for the employee's health care, dental care, vision care, and life insurance;
• Gender: gender as self-reported by employee;
• Ethnicity: ethnicity as self-reported by employee.

🔐 Dataset File 🔐

processed-payroll.csv

❓ Description ❓

This dataset consists in the payroll.csv without rows with Null values (all columns) and negative values (REGULAR_PAY, OVERTIME_PAY, ALL_OTHER_PAY, TOTAL_PAY, CITY_RETIREMENT_CONTRIBUTIONS and BENEFIT_PAY columns).

📝 Variables 📝

The file processed-payroll.csv contains the same variables of the payroll.csv file.

🔐 Dataset File 🔐

train-test-payroll.csv

❓ Description ❓

This dataset consists in the processed-payroll.csv containing just Active and Full-Time Employees, Labelled Categorical Variables and with a few less columns.

📝 Variables 📝

• Regular_Pay: regular work hours payment;
• Pay_Year: tax year employee was paid. This is not Fiscal Year;
• Department_NO: department Number in City Payroll System;
• Job_Class_PGrade: Job Class and Pay Grade;
• Job_Title: Job Title;
• Overtime_Pay: payments attributable to hours worked beyond regular work schedule;
• All_Other_Pay: any payments other than Regular and Overtime. This includes bonuses, adjustments, and lump sum payouts. Examples of bonuses include Permanent, Longevity, and Temporary Bonuses. Lump Sum Pay includes significant one-time payouts due to retirement, lawsuit settlements, or other adjustments;
• City_Retirement_Contributions: estimated payments made by the City towards employee's retirement;
• Benefit_Pay: city contribution for the employee's health care, dental care, vision care, and life insurance;
• Gender: gender as self-reported by employee;
• Ethnicity: ethnicity as self-reported by employee.

🎉 Acknowledgements 🎉

Thanks to:

1. Control Paneel L.A. for providing the data.

Context

Open Payments is a national disclosure program created by the Affordable Care Act (ACA) and managed by Centers for Medicare & Medicaid Services (CMS). The purpose of the program is to promote transparency into the financial relationships between pharmaceutical and medical device industries, and physicians and teaching hospitals. The financial relationships may include consulting fees, research grants, travel reimbursements, and payments from industry to medical practitioners.

Content

There are 3 datasets that represent 3 different payment types:

• General Payments: Payments not made in connection with a research agreement. This dataset contains 65 variables.

• Research Payments: Payments made in connection with a research agreement. This dataset contains 166 variables.

• Physician Ownership or Investment Interest: Information about physicians who hold ownership or investment interest in the manufacturer/GPO or who have an immediate family member holding such interest. This dataset contains 29 variables.

• Deleted/Removed Records: Contains any deleted/removed records.

A comprehensive methodology overview and data dictionary for each dataset can be found here.

Acknowledgements

The original datasets can be found here.

Inspiration

• Using the General Payments dataset, can you determine any trends in the total amount of payment to hospitals and physicians across the medical specialties or by the form/nature of the payments?

• According to the Research Payments dataset, which area(s) of research or the type of drug/medical device receive the most amount of payment?

This file contains anonymous responses from 206 participants to a survey on the use of BLIK mobile payments in Poland. The data were collected using an online questionnaire designed to analyze factors influencing the acceptance and usage of mobile payments via the BLIK system. Each row represents a single respondent, and each column corresponds to a specific survey question or demographic variable.

Data structure:

• Number of records (rows): 206

• Number of variables (columns): 27

• File format: Microsoft Excel (.xlsx)

• Survey language: Polish

Data scope:

The dataset includes responses to questions regarding attitudes toward mobile payments, skills, ease of use, social norms, satisfaction, and behavioral intentions related to BLIK usage. It also contains basic demographic information such as gender, age, education, employment status, place of residence, the way and frequency of using BLIK, and the bank where mobile payments are conducted.

Example columns:

• Do you use mobile payments via BLIK?

• I believe that BLIK mobile payment is useful in everyday life.

• Using BLIK mobile payment helps me perform tasks more quickly.

• I have the necessary skills to use BLIK mobile payment.

• Gender, Age, Education, Employment status, Current place of residence

• How often do you use BLIK?

• In which of the following banks do you conduct BLIK mobile payments?

Response format:

Most closed-ended questions are measured on a Likert scale (e.g., 1–7). Some questions are categorical or descriptive.

Potential uses:

The dataset can be used for research in the fields of electronic banking, fintech, consumer behavior, technology acceptance modeling, and demographic analyses of mobile payment users in Poland.

Notes:

The data are fully anonymized and do not contain any information that could identify respondents. If you use these data in publications, please cite the source appropriately.