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Recent research has demonstrated the capability of behavior signals capturedby smartphones and wearables for longitudinal behavior modeling. However, thereis a lack of a comprehensive public dataset that serves as an open testbed forfair comparison among algorithms. Moreover, prior studies mainly evaluatealgorithms using data from a single population within a short period, withoutmeasuring the cross-dataset generalizability of these algorithms. We presentthe first multi-year passive sensing datasets, containing over 700 user-yearsand 497 unique users' data collected from mobile and wearable sensors, togetherwith a wide range of well-being metrics. Our datasets can support multiplecross-dataset evaluations of behavior modeling algorithms' generalizabilityacross different users and years. As a starting point, we provide the benchmarkresults of 18 algorithms on the task of depression detection. Our resultsindicate that both prior depression detection algorithms and domaingeneralization techniques show potential but need further research to achieveadequate cross-dataset generalizability. We envision our multi-year datasetscan support the ML community in developing generalizable longitudinal behaviormodeling algorithms.

GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization