# Stackable trainings in the FAIRification and AI/ML readiness of data with applications to environmental health and justice

> **NIH NIH T32** · NORTHEASTERN UNIVERSITY · 2021 · $86,192

## Abstract

ABSTRACT
The ability to find, combine, and analyze multiple large-scale biomedical datasets to make better and ethical
decisions for the future of patients, populations, and health systems is now a set of necessary skills for modern
analysts. However, most current data analytics and workshops focus on deriving or applying modern
techniques, such as statistical learning procedures, PyTorch, TensorFlow, neural networks, and other
large-scale prediction models, as opposed to the necessary steps involved in preparing data for such analyses.
Further, the next (and current) generation of biomedical researchers must be cognizant of FAIR principles to be
prepared to make their data accessible by machines in order to fully leverage the continued growth around
methodological developments to properly analyze large amounts of data across multiple
studies/systems/countries. In addition to a methodologic toolkit, educating the biomedical analyst workforce
must include training to build their ability to locate and store data for future analyses in an automated manner.
We propose a suite of stackable modules to provide a rich foundation to the existing robust educational
offerings around the applications of AI/ML to biomedical data that many trainees already receive. Through our
close partnerships with the NIEHS PROTECT Center and the multinational OHDSI community for
observational health data science and informatics, our goal is to provide training to prepare data for AI and ML
applications in a rigorous and reproducible way, understand the ethical issues around AI and ML, as well as
receive hands-on training around FAIR principles for storing and accessing such data. These modules will
prepare researchers for successful careers as data analysts, ready to exploit the power of available AI/ML
frameworks.

## Key facts

- **NIH application ID:** 10405960
- **Project number:** 3T32ES023769-06A1S1
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** JULIA Green BRODY
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $86,192
- **Award type:** 3
- **Project period:** 2015-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10405960

## Citation

> US National Institutes of Health, RePORTER application 10405960, Stackable trainings in the FAIRification and AI/ML readiness of data with applications to environmental health and justice (3T32ES023769-06A1S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10405960. Licensed CC0.

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