The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

NaiveBayes_R.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given recidivism (P(x_ij│R)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|R): This tab contains probabilities of feature attributes among recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. Recidivated_Train: This tab contains re-coded features of recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|R) tab. NaiveBayes_NR.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given non-recidivism (P(x_ij│N)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|N): This tab contains probabilities of feature attributes among non-recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. NonRecidivated_Train: This tab contains re-coded features of non-recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given non-recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|N) tab. Training_LnTransformed.xlsx: Figures in each cell are log-transformed ratios of probabilities in NaiveBayes_R.xlsx (P(Xi|R)) to the probabilities in NaiveBayes_NR.xlsx (P(Xi|N)). TestData.xlsx: This Excel file includes the following tabs based on the test data: P(Xi|R), P(Xi|N), NIJ_Recoded, and Test_LnTransformed (log-transformed P(Xi|R)/ P(Xi|N)). Training_LnTransformed.dta: We transform Training_LnTransformed.xlsx to Stata data set. We use Stat/Transfer 13 software package to transfer the file format. StataLog.smcl: This file includes the results of the logistic regression analysis. Both estimated intercept and coefficient estimates in this Stata log correspond to the raw weights and standardized weights in Figure 1. Brier Score_Re-Check.xlsx: This Excel file recalculates Brier scores of Relaxed Naïve Bayes Classifier in Table 3, showing evidence that results displayed in Table 3 are correct. *****Full List***** NaiveBayes_R.xlsx NaiveBayes_NR.xlsx Training_LnTransformed.xlsx TestData.xlsx Training_LnTransformed.dta StataLog.smcl Brier Score_Re-Check.xlsx Data for Weka (Training Set): Bayes_2022_NoID Data for Weka (Test Set): BayesTest_2022_NoID Weka output for machine learning models (Conventional naïve Bayes, AdaBoost, Multilayer Perceptron, Logistic Regression, and Random Forest)

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

1. A readme file which describes the Python and Stata softwares we use to perform the data analysis and the data description. (filename: read me)
2. Two files which contain the Python codes. (filename: EM thesis python code)
3. One do file which contains the Stata code. (filename: EM thesis code)
4. An excel file which contains the data for generating our empirical results. (all interested variables and beta with control result v2)

Summary : Fuel demand is shown to be influenced by fuel prices, people's income and motorization rates. We explore the effects of electric vehicle's rates in gasoline demand using this panel dataset.

Files : dataset.csv - Panel dimensions are the Brazilian state ( i ) and year ( t ). The other columns are: gasoline sales per capita (ln_Sg_pc), prices of gasoline (ln_Pg) and ethanol (ln_Pe) and their lags, motorization rates of combustion vehicles (ln_Mi_c) and electric vehicles (ln_Mi_e) and GDP per capita (ln_gdp_pc). All variables are all under the natural log function, since we use this to calculate demand elasticities in a regression model.

adjacency.csv - The adjacency matrix used in interaction with electric vehicles' motorization rates to calculate spatial effects. At first, it follows a binary adjacency formula: for each pair of states i and j, the cell (i, j) is 0 if the states are not adjacent and 1 if they are. Then, each row is normalized to have sum equal to one.

regression.do - Series of Stata commands used to estimate the regression models of our study. dataset.csv must be imported to work, see comment section.

dataset_predictions.xlsx - Based on the estimations from Stata, we use this excel file to make average predictions by year and by state. Also, by including years beyond the last panel sample, we also forecast the model into the future and evaluate the effects of different policies that influence gasoline prices (taxation) and EV motorization rates (electrification). This file is primarily used to create images, but can be used to further understand how the forecasting scenarios are set up.

Sources: Fuel prices and sales: ANP (https://www.gov.br/anp/en/access-information/what-is-anp/what-is-anp) State population, GDP and vehicle fleet: IBGE (https://www.ibge.gov.br/en/home-eng.html?lang=en-GB) State EV fleet: Anfavea (https://anfavea.com.br/en/site/anuarios/)

1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables

2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)

3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)

4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)

5. Orignal PERCEIVE survey data (STATA file) and description of survey questions, descriptive results (word file)

The zip file includes one command file, one instruction, and data for the manuscript “Green Innovation Transformation, Economic Sustainability and Energy Consumption during China's New Normal Stage”. The Data file consists of two parts: the micro and macro empirical data. Both of them are uploaded by two types, dta format (for STATA software) and excel format. The Command.do file is uesed for the STATA software. The instruction describe how to use data and command files in steps.

The data used in this research is publicly available. The authors have put an Excel file and a STATA do file for the replications of all results shown in the paper.

Hypothesis: higher AUDIT scores are associated with increased risk of injuries among commercial motorcycle drivers in Dar es Salaam. Our data shows a four fold increase in risk among risky drinkers compared with non-drinkers. Structured questionnaire was used to data from motorcyclists and recorded in RedCap. Data was then exported into Excel and entered into Stata

Endovascular therapy (EVT) has changed the landscape of acute stroke treatment in the context of large vessel occlusion (LVO). Still, procedural success is typically determined by the degree of large vessel recanalization, despite the fact that large vessel recanalization does not always result in microvascular reperfusion. To address this discrepancy, we performed bedside optical CBF monitoring (with diffuse correlation spectroscopy) during endovascular therapy. This allowed comparison of CBF pre vs post-recanalization.Note 3 files uploaded:The .dta file is a stata file which contains all variables labels which includes all necessary variables details.The .xlsx file database is in numerical format (i.e. without applying text labels).The .xlsx file data dictionary contains all variable names, variable labels, and code to translate the numerical values.

Experimental data on intersectional discrimination against Arab Muslims in the Swedish rental housing market. Definitions for variables are in the Excel data file. The Stata do-file contains the complete analysis in accordance with the paper. The published paper can be accessed here: https://doi.org/10.1080/1369183X.2024.2366319.

File formats:

.xls: Excel file with variable names in 1. row and variable labels in 2. row

.xpt/.xpf: SAS XPORT data file (.xpt) and value labels (formats.xpf).

Note that the following variables were renamed in the output file: sumcadhssb -> SUMCADHS, sumcwursk -> SUMCWURS, adhdnotest -> ADHDNOTE, subs_subnotob -> SUBS_SUB, and that the internally recorded dataset name was shortened to "Liebrenz" .dta: Stata 13 data file

The file data.dta contains monthly water consumption data and survey variables used in the analysis. Estimations and calculations are done in Stata and Excel. Read Read-me.txt file for additional instructions.

Replication data set (Excel) and Stata .do file.

This is a replication package for the paper titled "Upstart Industrialization and Exports: Evidence from Japan, 1880–1910". The paper was published by the Journal of Economic History in 2018. The main file is a stata .do file. (ICPSR Replication- Meissner - Tang Upstart Industrialization - JEH - 092019.do) We used Stata 15 to create all Stata files. Running this .do file will create results from all tables and figures in the paper. The code from each table and figure is commented in the .do file. There are many auxiliary files that must be called. These can be found in the associated .zip file. There is one excel file included in the zip archive "Japan_trade_tables_figures-JEH-Meissner-Tang-Japan.xlsx" which has versions of the published tables and figures with a few other results not published in tabular form in the paper.

We have included a snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes at the bottom of the documentation file.

This version updates the set to the current turnovers as of May 30, 2022 version of Execucomp database and adds/clarifies several variables. Please check the documentation for the change log.

for updates check: https://zenodo.org/records/7591606

These are the data pertaining to the banking sector of the West African Economic and Monetary Union (UEMOA) . These data include an Excel database, a Stata dofile, and an M-file.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Product counterfeiting is the fraudulent reproduction of trademark, copyright, or other intellectual property related to tangible products without the authorization of the producer and motivated by the desire for profit. This study create a Product Counterfeiting Database (PCD) by assessing multiple units of analysis associated with counterfeiting crimes from 2000-2015: (1) scheme; (2) offender (individual); (3) offender (business); (4) victim (consumer); and (5) victim (trademark owner). Unique identification numbers link records for each unit of analysis in a relational database. The collection contains 5 Stata files and 1 Excel spreadsheet file. Scheme-Data.dta (n=196, 35 variables) Offender-Individual-Data.dta (n=551, 16 variables) Offender-Business-Data.dta (n=310, 5 variables) Victim-Consumer-Data.dta (n=54, 8 variables) Victim-Trademark-Owner-Data.dta (n=146, 5 variables) Relational-Data.xlsx (4 spreadsheet tabs)

A snapshot of the documentation file here to help with future use along with an Excel version of the file for non-STATA users. This document also includes information on submitting edits and corrections to the open source data, which we welcome and encourage. We will acknowledge the participation of editors in the versioning changes at the bottom of the Google Doc.

This version updates the set to the current turnovers as of May 30, 2022 version of Execucomp database and adds/clarifies several variables. Please check the documentation for the change log.

Replication data and do-file for Citi and Justesen (2014) "Measuring and explaining regulatory reform in the EU: A time-series analysis of eight sectors, 1984-2012", European Journal of Political Research 53(4), 709-726. Three files are available * Stata .dta file with replication data * Stata do-file to replicate results * Stata .dta and Excel files with regulatory data across eight different sectors The codebook is available in the online appendix at http://onlinelibrary.wiley.com/doi/10.1111/1475-6765.12061/abstract