# ABCD Course on Reproducible Data Analyses

> **NIH NIH R25** · FLORIDA INTERNATIONAL UNIVERSITY · 2021 · $86,381

## Abstract

PROJECT SUMMARY/ABSTRACT
The ABCD-ReproNim Course (1R25-DA051675) is a collaborative partnership to provide research educational
training in reproducible analyses of data from the ABCD Study. The course integrates curriculum from ReproNim:
A Center for Reproducible Neuroimaging Computation, which is a NIBIB-funded P41 Biomedical Technology
Resource Center (BTRC) whose vision is to help neuroimaging researchers achieve more reproducible data
analysis workflows and outcomes. The ReproNim approach relies on both technical development of readily
accessible, user-friendly computational tools and services that can be readily integrated into current research
practices, as well as a broad educational outreach about reproducibility to the neuroimaging community at large,
including developers as well as applied researchers across basic sciences and clinical disciplines. The current
project proposes an administrative supplement to provide dedicated research training on making data from the
Adolescent Brain Cognitive Development (ABCD) Study FAIR (i.e., Findable, Accessible, Interoperable, and
Reusable) and AI/ML (i.e., Artificial Intelligence and Machine Learning) ready. ML/AI applications have increased
relevance in the discovery of biomarkers, predicting intervention outcomes, and integrating information across
datasets. However, the knowledge required to perform effective biomedical ML research spans knowledge about
data, scientific questions, computing technologies alongside ML/AI platforms and tools. The ABCD-ReproNim
AI/ML Course will extend the current training to make trainees aware of the tools, concepts, and caveats for
multimodal ML/AI processing of ABCD data. Students will first receive training across a 5-week online course
that includes lectures, readings, and ABCD data exercises on topics that include: (1) FAIR for and FAIRness in
ML/AI Applications, (2) Core Concepts in ML, (3), Neuroimaging ML, (4) Interpretable/Explainable ML, and (5)
Introduction to Deep Learning. Competencies and skills addressed will include training and publishing ML
models, organizing and evaluating data for ML applications, and reusing existing models efficiently. Didactic
instruction will be followed by a 5-day remote Project Week, where students will apply the skills learned and work
towards completion of AI/ML data analysis projects. Success will result in well-trained researchers who are able
to apply reproducible AI/ML practices to test generalizability of AI/ML models for cross-sectional and longitudinal
prediction across the ABCD dataset.

## Key facts

- **NIH application ID:** 10406015
- **Project number:** 3R25DA051675-02S1
- **Recipient organization:** FLORIDA INTERNATIONAL UNIVERSITY
- **Principal Investigator:** David Nelson Kennedy
- **Activity code:** R25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $86,381
- **Award type:** 3
- **Project period:** 2020-07-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406015

## Citation

> US National Institutes of Health, RePORTER application 10406015, ABCD Course on Reproducible Data Analyses (3R25DA051675-02S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10406015. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*