生成可公开使用的长期工作与健康数据，支持研究与教育。
FLOW: A Feedback-Driven Synthetic Longitudinal Dataset of Work and Wellbeing
- 基于规则的反馈模拟，生成每日动态数据。
- 覆盖1000人两年数据，包含压力、睡眠等多维度指标。
- 适合缺乏真实数据时的方法验证与教学使用。
获取关于工作生活平衡与福祉的纵向个体级数据受限于隐私、伦理和后勤挑战，影响了压力建模、行为分析及机器学习等领域中可复现研究、方法基准测试和教学的开展。本文提出FLOW，一个基于规则、反馈驱动的合成纵向数据集，用于模拟工作量、生活方式与福祉之间的日常交互。该数据集通过模拟1,000名个体在两年内的每日动态，涵盖压力、睡眠、情绪、体力活动和体重等变量，具有高度的时间一致性。数据集已公开发布，并配套提供可配置的数据生成工具，支持在不同行为和情境假设下进行可复现实验。FLOW旨在作为可控实验环境，而非真实人群的替代品，适用于真实数据不可得时的探索性分析、方法开发与基准测试。
原文摘要 · Abstract (English)
Access to longitudinal, individual-level data on work-life balance and wellbeing is limited by privacy, ethical, and logistical constraints. This poses challenges for reproducible research, methodological benchmarking, and education in domains such as stress modeling, behavioral analysis, and machine learning. We introduce FLOW, a synthetic longitudinal dataset designed to model daily interactions between workload, lifestyle behaviors, and wellbeing. FLOW is generated using a rule-based, feedback-driven simulation that produces coherent temporal dynamics across variables such as stress, sleep, mood, physical activity, and body weight. The dataset simulates 1{,}000 individuals over a two-year period with daily resolution and is released as a publicly available resource. In addition to the static dataset, we describe a configurable data generation tool that enables reproducible experimentation under adjustable behavioral and contextual assumptions. 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。