Advanced Reproducibility in Cancer Informatics free videos and free material uploaded by Johns Hopkins University Staff .
Getting started in this course
This section describes the rationale and context for this course as well as its target audience
Defining Reproducibility
This section defines reproducibility for the purposes of this course
Version control with GitHub
This section discusses how to get started with creating branches and pull requests on GitHub
Code review - as an author
In this section we discuss the responsibility of an author of a pull request in code review
Code review -- as a reviewer
In this section we discuss the responsibility of a reviewer of a pull request in code review
Launching Docker
This section walks through how to get started with Docker
Modifying a Docker image
This section describes how to modify an existing Docker image
Automation as a reproducibility tool
This section describes the motivation for using automation tools to enhance reproducibility
This course introduces tools that help enhance reproducibility and replicability in the context of cancer informatics It uses hands-on exercises to demonstrate in practical terms how to get acquainted with these tools but is by no means meant to be a comprehensive dive into these tools The course introduces tools and their concepts such as git and GitHub, code review, Docker, and GitHub actions
Target Audience
The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research It is the follow up course to the Introduction to Reproducibility in Cancer Informatics course. Learners who take this course should:- Have some familiarity with R or Python
- Have take the Introductory Reproducibility in Cancer Informatics course
- Have some familiarity with GitHub
Motivation
Data analyses are generally not reproducible without direct contact with the original researchers and a substantial amount of time and effort (BeaulieuJones, 2017) Reproducibility in cancer informatics (as with other fields) is still not monitored or incentivized despite that it is fundamental to the scientific method Despite the lack of incentive, many researchers strive for reproducibility in their own work but often lack the skills or training to do so effectively
Equipping researchers with the skills to create reproducible data analyses increases the efficiency of everyone involved Reproducible analyses are more likely to be understood, applied, and replicated by others This helps expedite the scientific process by helping researchers avoid false positive dead ends Open source clarity in reproducible methods also saves researchers' time so they don't have to reinvent the proverbial wheel for methods that everyone in the field is already performing
Curriculum
The course includes hands-on exercises for how to apply reproducible code concepts to their code Individuals who take this course are encouraged to complete these activities as they follow along with the course material to help increase the reproducibility of their analyses
**Goal of this course:**
To equip learners with a deeper knowledge of the capabilities of reproducibility tools and how they can apply to their existing analyses scripts and projects
**What is NOT the goal of this course:**
To be a comprehensive dive into each of the tools discussed
How to use the course
Each chapter has associated exercises that you are encourage to complete in order to get the full benefit of the course
This course is designed with busy professional learners in mind -- who may have to pick up and put down the course when their schedule allows In general, you are able to skip to chapters you find a most useful to (One incidence where a prior chapter is required is noted)
Each chapter has associated exercises that you are encourage to complete in order to get the full benefit of the course
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