The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods.
This course is written for individuals who:
- Have some familiarity with R or Python - have written some scripts.
- Have not had formal training in computational methods.
- Have limited or no familiar with GitHub, Docker, or package management tools.
Motivation
Read more
The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods.
This course is written for individuals who:
- Have some familiarity with R or Python - have written some scripts.
- Have not had formal training in computational methods.
- Have limited or no familiar with GitHub, Docker, or package management tools.
Motivation
The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods.
This course is written for individuals who:
- Have some familiarity with R or Python - have written some scripts.
- Have not had formal training in computational methods.
- Have limited or no familiar with GitHub, Docker, or package management tools.
Motivation
Data analyses are generally not reproducible without direct contact with the original researchers and a substantial amount of time and effort (BeaulieuJones et al, 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
This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub.
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:**
Equip learners with reproducibility skills they can apply to their existing analyses scripts and projects. This course opts for an "ease into it" approach. We attempt to give learners doable, incremental steps to increase the reproducibility of their analyses.
**What is not the goal**
This course is meant to introduce learners to the reproducibility tools, but _it does not necessarily represent the absolute end-all, be-all best practices for the use of these tools_. In other words, this course gives a starting point with these tools, but not an ending point. The advanced version of this course is the next step toward incrementally "better practices".
How to use 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.
Each exercise has the option for you to continue along with the example files as you've been editing them in each chapter, OR you can download fresh chapter files that have been edited in accordance with the relative part of the course. This way, if you decide to skip a chapter or find that your own files you've been working on no longer make sense, you have a fresh starting point at each exercise.
What's inside
Syllabus
Introduction to this Course
In this first section, we will discuss the goals of this course and define what we mean by reproducibility.
Organizing your project
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Syllabus
Good to know
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Activities
Review R in preparation for course
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Review and practice basic R concepts to refresh your memory and enhance your understanding.
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R Programming
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-
Go over R syntax and data structures.
-
Practice writing simple R code to manipulate and analyze data.
-
Review R packages commonly used in data analysis.
Review R Programming Basics
Show steps
Provides stronger foundational skills in R, making concepts introduced in the early portions of this course easier to grasp.
Browse courses on
R Programming
Show steps
-
Practice writing and executing simple R scripts
-
Review basic R data types and structures
Join a peer study group on reproducibility
Show steps
Engage with peers in a study group dedicated to reproducibility, fostering collaboration and enhancing your understanding through shared experiences.
Show steps
-
Identify and join a peer study group focused on reproducibility.
-
Actively participate in discussions and share insights with group members.
-
Collaborate on projects or exercises related to reproducibility.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a reproducible data analysis project
Show steps
Immediately puts into practice concepts introduced by the course, such as project organization and version control.
Show steps
-
Define a research question and gather relevant data
-
Create a project directory and set up version control
-
Write reproducible data analysis scripts
-
Document your analysis and share your project
Follow tutorials on GitHub and Docker
Show steps
Enhance your understanding of GitHub and Docker through guided tutorials, enabling you to manage code and create reproducible environments.
Browse courses on
GitHub
Show steps
-
Complete tutorials on GitHub basics.
-
Practice using GitHub for version control and collaboration.
-
Explore Docker tutorials to learn about containerization.
Explore tutorials on code review and documentation
Show steps
Expand your knowledge of code review and documentation through guided tutorials, enhancing your ability to produce high-quality, well-documented code.
Browse courses on
Code Review
Show steps
-
Follow tutorials on code review techniques.
-
Practice reviewing code for clarity, efficiency, and reproducibility.
-
Review tutorials on effective documentation practices.
Practice writing reproducible code
Show steps
Strengthen your skills in writing reproducible code through repetitive exercises, improving the reliability and transparency of your analyses.
Browse courses on
Data Analysis
Show steps
-
Review principles of reproducible code.
-
Complete coding exercises that focus on writing reproducible code.
-
Practice using tools and techniques for debugging and testing reproducible code.
Write a blog post on best practices for reproducible analyses
Show steps
Solidify your understanding and share your knowledge by creating a blog post that summarizes best practices for conducting reproducible analyses, benefiting both yourself and others.
Browse courses on
Reproducibility
Show steps
-
Research and gather information on best practices for reproducible analyses.
-
Organize your thoughts and outline the content of your blog post.
-
Write the blog post clearly and concisely.
Contribute to an open-source project related to reproducibility
Show steps
Immerse yourself in the principles of reproducibility by contributing to an open-source project dedicated to developing tools or resources for reproducible analyses.
Browse courses on
Open Source
Show steps
-
Identify an open-source project focused on reproducibility.
-
Review the project's documentation and contribution guidelines.
-
Make a contribution to the project, such as reporting a bug, suggesting a feature, or writing code.
Review R in preparation for course
Show steps
Review and practice basic R concepts to refresh your memory and enhance your understanding.
Browse courses on
R Programming
Show steps
- Go over R syntax and data structures.
- Practice writing simple R code to manipulate and analyze data.
- Review R packages commonly used in data analysis.
Review R Programming Basics
Show steps
Provides stronger foundational skills in R, making concepts introduced in the early portions of this course easier to grasp.
Browse courses on
R Programming
Show steps
- Practice writing and executing simple R scripts
- Review basic R data types and structures
Join a peer study group on reproducibility
Show steps
Engage with peers in a study group dedicated to reproducibility, fostering collaboration and enhancing your understanding through shared experiences.
Show steps
- Identify and join a peer study group focused on reproducibility.
- Actively participate in discussions and share insights with group members.
- Collaborate on projects or exercises related to reproducibility.
Create a reproducible data analysis project
Show steps
Immediately puts into practice concepts introduced by the course, such as project organization and version control.
Show steps
- Define a research question and gather relevant data
- Create a project directory and set up version control
- Write reproducible data analysis scripts
- Document your analysis and share your project
Follow tutorials on GitHub and Docker
Show steps
Enhance your understanding of GitHub and Docker through guided tutorials, enabling you to manage code and create reproducible environments.
Browse courses on
GitHub
Show steps
- Complete tutorials on GitHub basics.
- Practice using GitHub for version control and collaboration.
- Explore Docker tutorials to learn about containerization.
Explore tutorials on code review and documentation
Show steps
Expand your knowledge of code review and documentation through guided tutorials, enhancing your ability to produce high-quality, well-documented code.
Browse courses on
Code Review
Show steps
- Follow tutorials on code review techniques.
- Practice reviewing code for clarity, efficiency, and reproducibility.
- Review tutorials on effective documentation practices.
Practice writing reproducible code
Show steps
Strengthen your skills in writing reproducible code through repetitive exercises, improving the reliability and transparency of your analyses.
Browse courses on
Data Analysis
Show steps
- Review principles of reproducible code.
- Complete coding exercises that focus on writing reproducible code.
- Practice using tools and techniques for debugging and testing reproducible code.
Write a blog post on best practices for reproducible analyses
Show steps
Solidify your understanding and share your knowledge by creating a blog post that summarizes best practices for conducting reproducible analyses, benefiting both yourself and others.
Browse courses on
Reproducibility
Show steps
- Research and gather information on best practices for reproducible analyses.
- Organize your thoughts and outline the content of your blog post.
- Write the blog post clearly and concisely.
Contribute to an open-source project related to reproducibility
Show steps
Immerse yourself in the principles of reproducibility by contributing to an open-source project dedicated to developing tools or resources for reproducible analyses.
Browse courses on
Open Source
Show steps
- Identify an open-source project focused on reproducibility.
- Review the project's documentation and contribution guidelines.
- Make a contribution to the project, such as reporting a bug, suggesting a feature, or writing code.
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