International Job Vacancies in Transport, Mobility, and Logistics: Employer Demands, Skills, Tools, and Educational Requirements (2025–2026)
Description
This dataset contains 2,181 international job-vacancy and occupational records related to transport, mobility, logistics, supply chain management, railway systems, freight transport, public transport, planning, operations, and associated professional profiles. The database covers the 2025–2026 period and includes 26 variables describing geographic scope, original and normalized job titles, employers, sources, education and experience requirements, employer demands, activities and responsibilities, requested skills, technological tools, standards and systems, knowledge requirements, and the relationship of each profile with Transport Engineering. The dataset was developed to identify current and emerging labor-market requirements and to support comparative analysis of professional competencies across countries and transport-related sectors. Original information was standardized to improve comparability among heterogeneous job advertisements, while derived variables were created to support thematic classification, evidence assessment, and analysis of implications for Transport Engineering education. The repository includes the master dataset in XLSX and CSV formats, a detailed data dictionary, observed categories and frequencies, data-quality summaries, standardization rules, and methodological notes. The dataset can be reused for research on transportation and logistics labor markets, professional skills, employer requirements, digital transformation, human-resource demand, engineering education, curriculum development, and international comparisons of occupational profiles. It may also support the identification of emerging competencies, software and technological requirements, and gaps between university curricula and industry needs.
Files
Steps to reproduce
1. Download the files 01_Base_Job_Vacancies.xlsx to 07_Standardization_Rules.csv and store them in the same working directory. 2. Use 01_Base_Job_Vacancies.xlsx as the master dataset. The workbook contains the complete database of 2,181 documented employment and occupational records related to transport, mobility, logistics, supply chain, infrastructure, professional skills, technological tools, and educational requirements. 3. Use 02_Base_Job_Vacancies.csv when an interoperable, machine-readable version of the dataset is required for statistical analysis, text mining, database import, or processing in R, Python, SPSS, Stata, or similar software. 4. Consult 03_Data_Dictionary_Base_Job_Vacancies.xlsx to identify the meaning, data type, structure, processing level, observed values, examples, and analytical purpose of each variable included in the dataset. 5. Use 04_Data_Dictionary_Base_Job_Vacancies.csv as the machine-readable version of the data dictionary when metadata need to be imported or processed programmatically. 6. Consult 05_Observed_Categories.csv to reproduce or verify the observed categorical values and their frequencies for the selected variables. These categories should be interpreted together with the definitions provided in the data dictionary. 7. Use 06_Quality_Summary.csv to verify dataset completeness, the number of non-missing observations, missing values, unique values, and the main distributional characteristics documented for each variable. 8. Consult 07_Standardization_Rules.csv before recoding, grouping, comparing, or analyzing textual and categorical variables. This file documents the harmonization and interpretation rules applied to equivalent terminology, categories, software names, professional requirements, and other standardized fields. 9. For reproducible analysis, maintain 01_Base_Job_Vacancies.xlsx unchanged as the reference dataset and perform analytical transformations on a copy of 02_Base_Job_Vacancies.csv. 10. Any additional filtering, tokenization, recoding, grouping, text-mining procedure, or statistical analysis should explicitly report the rules applied and should remain consistent with the definitions and standardization criteria documented in files 03 through 07.
Institutions
- Universidad Autónoma de la Ciudad de MéxicoMexico City, Mexico City