Stata file with sequential serological data, wide format (DTA format).

UNI-CEN Standardized Census Data Tables contain Census data that have been reformatted into a common table format with standardized variable names and codes. The data are provided in two tabular formats for different use cases. "Long" tables are suitable for use in statistical environments, while "wide" tables are commonly used in GIS environments. The long tables are provided in Stata Binary (dta) format, which is readable by all statistics software. The wide tables are provided in comma-separated values (csv) and dBase 3 (dbf) formats with codebooks. The wide tables are easily joined to the UNI-CEN Digital Boundary Files. For the csv files, a .csvt file is provided to ensure that column data formats are correctly formatted when importing into QGIS. A schema.ini file does the same when importing into ArcGIS environments. As the DBF file format supports a maximum of 250 columns, tables with a larger number of variables are divided into multiple DBF files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.

This dataset contains the entire data that was collected by the CHAIN prospective cohort study that was run between 01/11/2016 and 31/03/2019. The study was a multi-site longitudinal study that involved 9 sites in 6 countries. During the study, participant demographic, clinical, social, GPS and laboratory data was collected at various timepoints depending on a predetermined study activity schedule. This repository has been organized according to these broader domains. Each folder contains data files for that domain and these are in flat/wide format. Each domain folder has specific subdomain files, for instance, demographic contains anthropometry, dates and outcome flat files. Each folder contains both .csv and .dta (stata data file), however we recommend using the .csv files whenever possible as this is the generated file by the main reproducible script. Further to these folders, a note-to-file folder has been added that contains data cleaning notes for specific unresolvable queries that explain those data. Data dictionaries have been provided in two kinds: a main wide codebook of every variable and a leaner data domains file that contains specific variables per domain.

Description

This dataset contains data from an online experiment designed to test whether economically equivalent penalties—fees (paid before taking) and fines (paid after taking)—influence prosocial behaviour differently. Participants played a modified dictator game in which they could take points from another participant.

The dataset is provided in Excel format (Full-data.xlsx), along with a Stata do-file (submit.do) that reshapes, cleans, and analyses the data.

Data Collection

• Platform: oTree

• Recruitment: Prolific

• Sample size: 201 participants

• Design: Each participant played 20 rounds: 10 in the control condition and 10 in one treatment condition (fee or fine). Order of blocks was randomised.

• Payment: 200 points = £1. One round was randomly selected for payment.

Variables

Identification

• session – Session number

• id – Participant ID

• treatment – Assigned treatment (1 = Fee, 2 = Fine)

• order – Order of blocks (0 = Control first, 1 = Treatment first)

Decision Rounds

For each round, participants made decisions in both control (c) and treatment (t) conditions.

• c1, t1, c2, t2, … – Tokens available and/or allocated across control and treatment rounds.

• takeX – Amount taken from the other participant in case X.

Norm Elicitation

Social norms were elicited after the taking task. Variables include empirical, normative, and responsibility measures at both extensive and intensive margins:

• eyX, etX – Empirical expectations (beliefs about what others do)

• nyX, ntX – Normative expectations (beliefs about what others think is appropriate)

• ryX, rtX – Responsibility measures

• casenormX – Case identifier for norm elicitation

Demographics

From survey responses:

• Sex – Gender

• Ethnicitysimplified – Simplified ethnicity category

• Countryofresidence – Participant’s country of residence

Other

• order, session – Experimental setup metadata

Stata Do-File (analysis.do)

The .do file performs the following steps:

1. Data Preparation

   • Import raw Excel file

   • Reshape from wide to long format (cases per participant)

   • Declare panel data (xtset id)

2. Variable Generation

   • Rename variables for clarity (e.g., take for amount taken)

   • Generate treatment dummies (treat)

   • Construct demographic dummies (gender, race, nationality)

3. Analysis Preparation

   • Create extensive and intensive margin variables

   • Generate expectation and norm measures

4. Output

   • Ready-to-analyse panel dataset for regression and statistical analysis

Data are from a multi-centre, parallel-group randomised controlled trial. The original protocol for this study is available from (URL: http://idmr.scu.edu.cn/info.htm?id=1841614474692833). This study was registered at the Chinese Clinical Trial Registry on 11 Apr 2020 (ChiCTR2000031834). Ethical approval was first received from the Institutional Review Board (IRB) of the First Affiliated Hospital of Nanjing Medical University/Jiangsu Province Hospital (2020-SR-171, 9 April 2020) and then subsequently from the IRBs of Hubei Province Hospital of Integrated Chinese and Western Medicine (2020016, 14 April 2020), and Huangshi Hospital of Chinese Medicine (HSZYPJ-2020-026-01, 20 April 2020).

Three centres from Jiangsu (The First Affiliated Hospital of Nanjing Medical University), Hubei Wuhan (Hubei Province Hospital of Integrated Chinese and Western Medicine), and Hubei Huangshi (Hubei Huangshi Hospital of Chinese Medicine) collected data from 119 patients recovering from COVID-19 ...

UNI-CEN Standardized Census Data Tables contain Census data that have been reformatted into a common table format with standardized variable names and codes. The data are provided in two tabular formats for different use cases. "Long" tables are suitable for use in statistical environments, while "wide" tables are commonly used in GIS environments. The long tables are provided in Stata Binary (dta) format, which is readable by all statistics software. The wide tables are provided in comma-separated values (csv) and dBase 3 (dbf) formats with codebooks. The wide tables are easily joined to the UNI-CEN Digital Boundary Files. For the csv files, a .csvt file is provided to ensure that column data formats are correctly formatted when importing into QGIS. A schema.ini file does the same when importing into ArcGIS environments. As the DBF file format supports a maximum of 250 columns, tables with a larger number of variables are divided into multiple DBF files. For more information about file sources, the methods used to create them, and how to use them, consult the documentation at https://borealisdata.ca/dataverse/unicen_docs. For more information about the project, visit https://observatory.uwo.ca/unicen.