# Data Science Foundations: Tools and Statistics for Modern Biomedical Research in the Cloud

> **NIH NIH T32** · DUKE UNIVERSITY · 2024 · $151,556

## Abstract

Abstract
Data exploration, analysis, and visualization are integral to modern pharmacological research. We propose a
cloud-based learning module focused on best practices in these areas, with an emphasis on creating repro-
ducible workflows and understanding statistical principles relevant to large datasets. This module will support
research training under the parent T32 ‘Pharmacological Sciences Training Grant’ by enabling trainees to effec-
tively employ cloud computing and establish reproducible data analysis workflows with JupyterLab Notebooks
and GitHub. It will also facilitate the application of contemporary software tools for data analysis and visualiza-
tion, and provide a solid understanding of the key statistical principles involved. In summary, this module will
offer a comprehensive educational experience in data science and statistical principals, tailored specifically for
large genomic datasets. Its reach extends beyond our T32 trainees, including biomedical graduate students from
various disciplines. Additionally, it caters to an indeterminate number of online learners, broadening its impact
and accessibility in the field of pharmacological research.

## Key facts

- **NIH application ID:** 11031455
- **Project number:** 3T32GM133352-05S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Cynthia M Kuhn
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $151,556
- **Award type:** 3
- **Project period:** 2020-07-01 → 2025-08-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11031455

## Citation

> US National Institutes of Health, RePORTER application 11031455, Data Science Foundations: Tools and Statistics for Modern Biomedical Research in the Cloud (3T32GM133352-05S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11031455. Licensed CC0.

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