This is part of the global project on work addiction, which was financed by the National Science Centre, Poland, under grant no. 2020/39/D/HS6/00198 (“The role of macro-, meso-, and micro-level factors in work addiction and related health problems”). The project is led by the consortium of the University of Silesia in Katowice (Prof. Edyta Charzyńska) and the University of Gdańsk (Prof. Paweł A. Atroszko). The dataset “Data” (CSV UTF-8 and XLSX formats) contains data collected online from 31,352 employees from six continents and 85 cultures. This data was used to develop a short cross-culturally invariant work addiction scale (International Work Addiction Scale – IWAS) that can be utilized globally. Data collection took place online between autumn 2022 and autumn 2023, with the cooperation of over 100 researchers worldwide. Statistical analysis included confirmatory factor analysis (CFA) to establish the structure of the IWAS, multi-group CFA (MGCFA) to examine measurement invariance across cultures, and latent profile analysis (LPA) to identify the profiles of symptoms of work addiction. Additionally, descriptive statistics, reliability, convergent validity, test accuracy, classification consistency, prevalence rates of work addiction, and correlations with criterion variables (job stress, job satisfaction, and self-esteem) were calculated and analyzed. Analyses were performed using MPlus version 8.0 (Muthén & Muthén, 2017) and IBM SPSS Statistics version 28.0 (IBM Corp., 2021). The results indicate that the IWAS is a valid, reliable, and short screening scale that can be used in different cultures and languages, providing comparative results that allow for broad generalizations and investigation of the work addiction phenomenon worldwide. Funding sources: This work was supported by the National Science Centre, Poland [grant number 2020/39/D/HS6/00198]. Data collection in Armenia was supported by the Science Committee of the Republic of Armenia in the frames of the research project 25RG-5A025. Data collection in the Czech Republic was supported by financing from NPO “Systemic Risk Institute” no. LX22NPO5101, funded by European Union - Next Generation EU (Ministry of Education, Youth and Sports, NPO: EXCELES). Data collection in Hungary was supported by the Hungarian National Research, Development, and Innovation Office (Grant number: FK134807) and the Bolyai János Research Scholarship of the Hungarian Academy of Sciences granted to Bernadette Kun. Files attached: (i) Dataset; (ii) Codebook; (iii) MPlus script for confirmatory factor analysis (CFA), (iv) MPlus script for multi-group CFA (MGCFA), (v) MPlus script for latent profile analysis (LPA); and (vi) README file.