The Alcohol and Drug Abuse Research Program (ADARP) dataset was collected as a part of a pilot study that aimed to discover how the daily experiences of patients diagnosed with alcohol use disorder (AUD) correspond with physiological biomarkers of stress. Each participant completed three components: 1) a daily diary study using ecological momentary assessment (EMA) of self-reported emotions, cravings, and stress via a web-based survey, prompted 4 times daily for up to 14 days; 2) continuous monitoring of stress with an Empatica E4 wristband that captured, in real-time, continuous physiological markers of stress, including heart rate (HR), skin conductance or electrodermal activity (EDA), skin temperature, and bodily movements; and 3) structured qualitative interviews to assess daily alcohol use, using a timeline follow-back calendar, and to validate self-reported and physiological markers of stress. With the proposed study, we aimed to address three research objectives. Aim 1 examines the fluctuations and associations among the self-reported emotions, alcohol-related cravings, and stress assessed in the EMA component. Analysis for Aim 1 centered on concurrent and lagged associations among negative affect, cravings, and stress. Aim 2 uses the continuous monitoring data acquired via the wearable device to visualize and describe fluctuations among the physiological measures of stress. Analysis for this aim focused on determining the degree of synchrony among the different physiological indicators. Aim 3 combines the EMA and continuous monitoring components in order to determine if self-reported changes in alcohol-related cravings can be predicted from physiological measures of stress. ADARP dataset was collected from participants suffering from alcohol use disorder (AUD) and receiving treatment at an outpatient treatment agency during 2019 - 2020, at Washington State University (WSU), Pullman, WA, USA. Funding for the original study was provided by the Alcohol and Drug Research Program (ADARP) of Washington State University. This investigation was supported in part by funds provided for medical and biological research by the State of Washington Initiative Measure No. 171. The sensor and EMA survey data are made public to facilitate further research into this topic. We have also set up a GitHub repository with code that can be used to process the sensor data and extract meaningful information. Please cite the following papers if you use this dataset in your research. Ramesh Kumar Sah, Hassan Ghasemzadeh, Assal Habibi, Michael McDonell, Patricia Pendry, and Michael Cleveland. 2020. Poster: Mobile Health for Alcohol Recovery and Relapse Prevention. In 2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE Press, 18–19. https://doi.org/10.1145/3384420.3431779 Alinia P, Sah RK, McDonell M, Pendry P, Parent S, Ghasemzadeh H, Cleveland MJ. Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study. JMIR Form Res. 2021 Jul 21;5(7):e27891. DOI: 10.2196/27891. PMID: 34287205; PMCID: PMC8339978. Ramesh Kumar Sah, Michael McDonell, Patricia Pendry, Sara Parent, Hassan Ghasemzadeh, Michael J Cleveland. ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. ArXiv, 2022, Jun. If you have any questions or suggestions, please feel free to reach Ramesh Sah at ramesh.sah@wsu.edu