FLOW: Synthetic Dataset for Work and Wellbeing Research
Published:Dec 28, 2025 14:54
•1 min read
•ArXiv
Analysis
This paper introduces FLOW, a synthetic longitudinal dataset designed to address the limitations of real-world data in work-life balance and wellbeing research. The dataset allows for reproducible research, methodological benchmarking, and education in areas like stress modeling and machine learning, where access to real-world data is restricted. The use of a rule-based, feedback-driven simulation to generate the data is a key aspect, providing control over behavioral and contextual assumptions.
Key Takeaways
- •Introduces FLOW, a synthetic longitudinal dataset for work and wellbeing research.
- •Addresses limitations of real-world data access due to privacy and ethical concerns.
- •Uses a rule-based, feedback-driven simulation to generate the dataset.
- •Provides a configurable data generation tool for reproducible experimentation.
- •Aims to support exploratory analysis, methodological development, and benchmarking.
Reference
“FLOW is intended as a controlled experimental environment rather than a proxy for observed human populations, supporting exploratory analysis, methodological development, and benchmarking where real-world data are inaccessible.”