# Developing A Diverse Workforce: Advancing Data Science for Addiction Research and Professional Training (ADAPT)

> **NIH NIH R25** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2024 · $136,320

## Abstract

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 d...

## Key facts

- **NIH application ID:** 11015112
- **Project number:** 1R25DA061740-01
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** MEREDITH C. B. ADAMS
- **Activity code:** R25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $136,320
- **Award type:** 1
- **Project period:** 2024-09-15 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11015112

## Citation

> US National Institutes of Health, RePORTER application 11015112, Developing A Diverse Workforce: Advancing Data Science for Addiction Research and Professional Training (ADAPT) (1R25DA061740-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11015112. Licensed CC0.

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