This textbook was written for the clinical research community at Johns Hopkins leveraging the precision medicine analytics platform (PMAP). These notebooks are available in html form on the Precision Medicine portal as well as in computational form in the CAMP-share folder on Crunchr (

PMAP Cookbook

The PMAP Cookbook is a textbook of computational notebooks to aid researchers at Johns Hopkins conduct clinical research. The Cookbook is used for the CAMP training program. The goal of this textbook is to accelerate clinical research by providing examples of how to work with clinical data * within the platform tools at JHM (Crunchr/Databricks) using a combination of R & Python programming languages and modern data science libraries.

The PMAP Cookbook is a textbook of recipes for analysis on the PMAP platform. This textbook is written as a navigable collection of computational Jupyter notebooks that cover analysis of EMR data, medical imaging, physiological monitoring waveform, and genomic sequencing. Jupyter notebooks are an open standard JSON format that encapsulates the analytics workproduct and serve as a best practice in reproductible research.

Textbook examples will emphasis the key steps of data science following exploratory data analysis, data cleaning, feature extraction, model building and validation. The Cookbook will also provide as a user’s guide for use of platform tools provided to clinical researchers.

The textbook will be published as static html pages on the Precision Medicine Portal as well as computational ipython notebooks on a shared public getted-started folder on Crunchr. Many of the tutorials will leverage de-identified clinical data derived from JHM. The Cookbook will include the workflow for authorized users to attest to terms of use of these data sources.

Editor: Paul Nagy, PhD


This cookbook is a product of many authors. Consider sharing your notebook for inclusion of best practices on conducting research on clinical data at Johns Hopkins.