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Reproducible Research

Roger D. Peng, PhD, Jeff Leek, PhD, and Brian Caffo, PhD

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.

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What's inside

Syllabus

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.

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Read about what's good

what should give you pause

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Teaches skills highly relevant in academia

Develops skills for publishing same outcome analysis as peers

Taught by instructors recognized for their work in reproducible research

Part of a multi-modal learning experience

Builds a foundation for beginners in reproducible research

Provides hands-on experience in reproducible research tools

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Reviews summary

Introduction to reproducible data analysis

According to learners, the Reproducible Research course offers a solid introduction to a crucial concept in data analysis. Many found the focus on practical tools like knitr and Markdown to be a major strength, enabling them to publish analyses in a reproducible manner. The course effectively explains why reproducibility matters through concepts and case studies. While some appreciated the hands-on assignments that reinforced learning, others found the technical aspects challenging, especially without prior R knowledge. Overall, students agree the course provides a valuable foundation, though some wished for deeper coverage.

Provides good foundation, less depth on advanced topics.

"Great as an introduction, but doesn't go very deep into complex scenarios."

"Felt a bit basic if you already have some experience with R Markdown."

"Wish it covered more advanced topics like package development for reproducibility."

"Good overview, but plan on further learning for production-level work."

Projects help reinforce understanding.

"The assignments were challenging but really solidified the concepts."

"I learned the most by actually doing the final assignment."

"The peer assessment required me to apply everything learned."

"Doing the reproducible data analysis project was very practical."

Teaches useful tools like knitr and Markdown.

"knitr and Markdown were new to me, and they are incredibly useful."

"The hands-on labs using these tools were excellent."

"Now I can create dynamic documents easily."

"I really appreciated learning specific tools to implement reproducibility."

Highlights the importance of reproducibility.

"Learning about reproducible research is vital for any data scientist."

"The course hammered home why I need to make my analysis reproducible."

"This is a concept I wish I learned much earlier in my career."

"Understanding the ethical and practical reasons for reproducibility was key."

May be difficult without prior R experience.

"If you're new to R, be prepared for a steep learning curve on top of the course material."

"Requires a basic understanding of R to keep up with the examples."

"The course doesn't teach R, just uses it, which was tough for a beginner."

"Had to supplement with external R tutorials to manage the assignments."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Reproducible Research with these activities:

Find a mentor in the field of reproducible research

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Seeking guidance from experienced professionals in the field can greatly enhance your learning journey.

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Identify potential mentors in your network or through online platforms
Reach out to mentors and express your interest
Set up regular meetings to discuss your progress and seek advice
Attend events or workshops where you can connect with potential mentors

Review R markdown and knitr

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These tools will be used extensively throughout this course and can serve as a foundation to help improve development of reproducible documents.

Browse courses on R Markdown

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Review the R Markdown documentation
Review the knitr documentation
Create a simple R Markdown document
Knit the R Markdown document to HTML
Explore the features of R Markdown and knitr

Create a reproducible report using R Markdown

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This course focuses on the usage of R Markdown for creating reproducible data analyses and scientific reports, and this activity serves as a practical application of those concepts.

Browse courses on Report Writing

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Choose a dataset and analyze it
Write a report in R Markdown
Knit the report to HTML
Share the report with others
Get feedback on the report

Four other activities

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Participate in peer review of reproducible reports

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The course emphasizes the importance of peer review in the process of creating reproducible reports, and this activity allows students to practice this.

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Find a peer to review your report
Review the report and provide feedback
Incorporate the feedback into your report
Discuss the review process with your peer

Create a collection of resources on reproducible research

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This activity encourages students to immerse themselves in the topic by compiling relevant resources that expand upon the course's materials.

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Search for articles, tutorials, and other resources on reproducible research
Organize the resources into a structured format
Add annotations and summaries to each resource
Share the collection with other students

Contribute to an open-source data science project

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Contributing to an open-source project not only reinforces the concepts learned in the course but also provides an opportunity to make a tangible impact on the community.

Browse courses on Collaboration

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Find an open-source data science project to contribute to
Review the project's documentation and codebase
Identify an area where you can contribute
Make your contributions and submit a pull request
Collaborate with other contributors and the project maintainers

Develop a reproducible data analysis workflow for a real-world dataset

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This activity provides an opportunity for students to apply their skills to a practical problem.

Browse courses on Project-Based Learning

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Choose a real-world dataset
Define the research question or problem you want to solve
Develop a reproducible data analysis workflow
Write a report summarizing your findings
Share your project with others

Career center

Learners who complete Reproducible Research will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, cleaning, and analyzing data to extract insights and inform decision-making. This course in Reproducible Research would be a valuable asset to aspiring Data Scientists as it provides a foundation in the principles and tools for conducting and reporting reproducible data analyses. The course emphasizes the importance of transparency, replicability, and collaboration in data science, which are essential skills for professionals in this field.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. The course in Reproducible Research is highly relevant to this field, as it focuses on the concepts and tools for reporting modern data analyses in a reproducible manner. The course covers topics such as literate statistical analysis tools, which allow Statisticians to publish data analyses in a single document that can be easily executed by others to obtain the same results.

See salaries and explore the career path for Statistician

Research Scientist

Research Scientists conduct scientific research to advance knowledge and develop new technologies. The course in Reproducible Research is highly applicable to this field, as it provides a foundation in the principles and practices of reproducible research. The course emphasizes the importance of transparency, replicability, and collaboration in scientific research, which are essential skills for Research Scientists.

See salaries and explore the career path for Research Scientist

Data Analyst

Data Analysts use data to identify trends, patterns, and insights that can help businesses make better decisions. This course in Reproducible Research is highly relevant to this field, as it provides a foundation in the principles and tools for conducting and reporting reproducible data analyses. The course emphasizes the importance of transparency, accuracy, and collaboration in data analysis, which are essential skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course in Reproducible Research would be beneficial to aspiring Software Engineers as it provides a foundation in the principles and practices of software development. The course emphasizes the importance of modularity, testing, and documentation in software development, which are essential skills for Software Engineers.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. This course in Reproducible Research would be beneficial to aspiring Machine Learning Engineers as it provides a foundation in the principles and practices of machine learning. The course emphasizes the importance of data quality, feature engineering, and model evaluation in machine learning, which are essential skills for Machine Learning Engineers.

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Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. This course in Reproducible Research would be beneficial to aspiring Data Engineers as it provides a foundation in the principles and practices of data engineering. The course emphasizes the importance of data quality, data integration, and data security in data engineering, which are essential skills for Data Engineers.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data. This course in Reproducible Research would be beneficial to aspiring Quantitative Analysts as it provides a foundation in the principles and practices of quantitative analysis. The course emphasizes the importance of data analysis, modeling, and forecasting in quantitative analysis, which are essential skills for Quantitative Analysts.

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Business Analyst

Business Analysts use data and analysis to help businesses make better decisions. This course in Reproducible Research would be beneficial to aspiring Business Analysts as it provides a foundation in the principles and practices of data analysis. The course emphasizes the importance of data collection, data analysis, and data visualization in business analysis, which are essential skills for Business Analysts.

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Epidemiologist

Epidemiologists investigate the causes and patterns of disease and injury in populations. This course in Reproducible Research would be beneficial to aspiring Epidemiologists as it provides a foundation in the principles and practices of epidemiology. The course emphasizes the importance of data collection, data analysis, and data interpretation in epidemiology, which are essential skills for Epidemiologists.

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Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course in Reproducible Research would be beneficial to aspiring Actuaries as it provides a foundation in the principles and practices of actuarial science. The course emphasizes the importance of data analysis, modeling, and forecasting in actuarial science, which are essential skills for Actuaries.

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Biostatistician

Biostatisticians use statistical methods to design and analyze studies in the health sciences. This course in Reproducible Research would be beneficial to aspiring Biostatisticians as it provides a foundation in the principles and practices of biostatistics. The course emphasizes the importance of data analysis, modeling, and forecasting in biostatistics, which are essential skills for Biostatisticians.

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Data Journalist

Data Journalists use data to tell stories and inform the public. This course in Reproducible Research would be beneficial to aspiring Data Journalists as it provides a foundation in the principles and practices of data journalism. The course emphasizes the importance of data collection, data analysis, and data visualization in data journalism, which are essential skills for Data Journalists.

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UX Researcher

UX Researchers conduct research to improve the user experience of products and services. This course in Reproducible Research would be beneficial to aspiring UX Researchers as it provides a foundation in the principles and practices of UX research. The course emphasizes the importance of data collection, data analysis, and data interpretation in UX research, which are essential skills for UX Researchers.

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Market Researcher

Market Researchers conduct research to understand the needs and wants of consumers. This course in Reproducible Research would be beneficial to aspiring Market Researchers as it provides a foundation in the principles and practices of market research. The course emphasizes the importance of data collection, data analysis, and data interpretation in market research, which are essential skills for Market Researchers.

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Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Reproducible Research.

Reproducible Research with R and RStudio

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Provides a comprehensive introduction to reproducible research in R. It covers topics such as version control, data management, and literate programming. It valuable resource for practitioners who want to learn how to conduct reproducible research.

Reproducible Research

Two deals to help you save

What's inside

Syllabus

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Reviews summary

Introduction to reproducible data analysis

Activities

Career center

Reading list

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