DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

2025-02-05

AI Summary

Existing smartphone behavioral datasets suffer from narrow national coverage, small sample sizes, and limited sensor modalities, severely hindering cross-cultural behavioral modeling and rigorous evaluation of model generalizability. To address these limitations, we introduce the largest and most diverse publicly available multinational smartphone behavioral dataset to date: collected over four consecutive weeks from 782 university students across eight countries spanning the Global North and South. It comprises 26 types of raw sensor time-series data and over 350,000 fine-grained, context-aware self-reports, enriched with demographic, psychological, and socio-cultural metadata. This project pioneers standardized, cross-cultural collaborative data collection under a unified data governance protocol. The dataset substantially enhances reproducibility in cross-national behavioral modeling and enables robust domain adaptation research. As foundational infrastructure for ubiquitous computing, it advances empirical investigation of model robustness and generalization across culturally and geographically diverse populations.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.

Problem

• Cross-country behavior modeling
• Smartphone sensor data diversity
• Domain adaptation and generalization

Innovation

• Multi-country smartphone sensor dataset
• 26 sensor modalities data collection
• 350K+ self-reports analysis