Reproducible Research

About this course: This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

Created by:  Johns Hopkins University

• Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Brian Caffo, PhD, Professor, Biostatistics

  Bloomberg School of Public Health

Basic Info Course 5 of 10 in the Data Science Specialization Commitment 4-9 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Johns Hopkins University The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus


WEEK 1


Week 1: Concepts, Ideas, & Structure



This week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.


9 videos, 3 readings expand

1. Video: Introduction
2. Reading: Syllabus
3. Reading: Pre-course survey
4. Reading: Course Book: Report Writing for Data Science in R
5. Video: What is Reproducible Research About?
6. Video: Reproducible Research: Concepts and Ideas (part 1)
7. Video: Reproducible Research: Concepts and Ideas (part 2)
8. Video: Reproducible Research: Concepts and Ideas (part 3)
9. Video: Scripting Your Analysis
10. Video: Structure of a Data Analysis (part 1)
11. Video: Structure of a Data Analysis (part 2)
12. Video: Organizing Your Analysis

Graded: Week 1 Quiz

WEEK 2


Week 2: Markdown & knitr



This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.


9 videos expand

1. Video: Coding Standards in R
2. Video: Markdown
3. Video: R Markdown
4. Video: R Markdown Demonstration
5. Video: knitr (part 1)
6. Video: knitr (part 2)
7. Video: knitr (part 3)
8. Video: knitr (part 4)
9. Video: Introduction to Course Project 1

Graded: Week 2 Quiz
Graded: Course Project 1

WEEK 3


Week 3: Reproducible Research Checklist & Evidence-based Data Analysis



This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.


10 videos expand

1. Video: Communicating Results
2. Video: RPubs
3. Video: Reproducible Research Checklist (part 1)
4. Video: Reproducible Research Checklist (part 2)
5. Video: Reproducible Research Checklist (part 3)
6. Video: Evidence-based Data Analysis (part 1)
7. Video: Evidence-based Data Analysis (part 2)
8. Video: Evidence-based Data Analysis (part 3)
9. Video: Evidence-based Data Analysis (part 4)
10. Video: Evidence-based Data Analysis (part 5)

WEEK 4


Week 4: Case Studies & Commentaries
This week there are two case studies involving the importance of reproducibility in science for you to watch.


5 videos, 1 reading expand

1. Video: Caching Computations
2. Video: Case Study: Air Pollution
3. Video: Case Study: High Throughput Biology
4. Video: Commentaries on Data Analysis
5. Video: Introduction to Peer Assessment 2
6. Reading: Post-Course Survey

Graded: Course Project 2