Project Summary As we work to build addiction data science literacy, our field could benefit from increased diversity of background and perspectives in the workforce. The complexities of addiction as a clinical domain present challenges, including the intersection of mental health and chronic pain. Understanding how these factors influence data collection, often due to subjective reporting, the influence of stigma, health disparities, and longstanding barriers to care, can impact data analytics and interpretation. Moreover, the separation between clinical and data experts can create additional challenges to advancing the field. Aligning the addiction background with data science expertise could enhance the potential of emerging addiction researchers. Another impediment to progress is a need for more diversity in our workforce, which could be partly attributed to a lack of awareness of the field during training (undergraduate to postgraduate).Developing training for a diverse workforce that understands challenges at the intersection of addiction and data science will accelerate our understanding of addiction's complexity. The long-term goal of this Developing a Diverse Workforce: Advancing Data Science for Addiction Research and Professional Training (ADAPT) R25 application is to support the training of a diverse workforce by building an addiction data science short course and scalable educational content with a focus on addiction data analytics through a health equity lens. The overall objective of this proposal is to provide the curated research framework and resources to support emerging investigators with diverse data science addiction approaches. Our central hypothesis involves developing an addiction data science training program that will expand the research capacity of diverse emerging investigators. We will achieve the goals of this proposal through the following aims: Aim 1- Develop and refine immersive, tailored addiction data science skills course that provides hands-on demonstrations, tutorials, and presentations on FAIR (Findable, Accessible, Interoperable, Reusable) data principles, computational analytical methods (AI and ML), systems modeling, NLP, and analysis and linking of addiction big data. Aim 2- Incorporate novel methods of program evaluation and dissemination, which will include leveraging NLP modalities to mine academic databases using advanced analytics to capture participant outcomes. At the successful completion of the proposed research, the expected outcome is a scalable and widely disseminated education intervention for addiction data science with enduring content to support emerging researchers, removing many of the barriers to traditional pathways (e.g., asynchronous conceptual and project-based content that is widely available). This R25 ADAPT project will provide a strong basis for the conceptual foundation needed to begin addiction data science research without sustained effort from a limited pool of addiction data science experts. This research aligns with the mission of NIH NIDA to train a diverse workforce to perform addiction data science research at the highest quality levels.

Project Narrative This proposal is relevant to public health because it works to unlock the complex intersection of addiction and big data for a more diverse workforce. Creating curated and scalable training for a broad audience of trainees can enhance our ability to solve problems in addiction science. This research aligns with NIH NIDA's goal to provide the highest quality research training to a diverse workforce.