Introduction

Today the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.

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To meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery.

This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project.

We’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.

Consider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure - and success - stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research, and to encourage and integrate with similar environments for your collaborators and colleagues. We look forward to seeing you in this course and the research you do in the future!

What you'll learn:

  • Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.
  • Fundamentals of reproducible science using case studies that illustrate various practices
  • Key elements for ensuring data provenance and reproducible experimental design
  • Statistical methods for reproducible data analysis
  • Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows.
  • How to develop new methods and tools for reproducible research and reporting
  • How to write your own reproducible paper.

Meet The Author

Curtis Huttenhower

Curtis Huttenhower

Associate Professor of Computational Biology and Bioinformatics, Harvard University

Dr. Curtis Huttenhower is an associate professor of Computational Biology and Bioinformatics in the Biostatistics Department at the Harvard T.H. Chan School of Public Health and director of the Huttenhower Lab. His research focuses on understanding the function of microbial communities, particularly that of the human microbiome in health and disease. This work entails a combination of computational methods development for wrangling large data collections, as well as biological analyses and laboratory experiments to link the microbiome in human populations to specific microbiological mechanisms.

John Quackenbush

John Quackenbush

Professor of Computational Biology and Bioinformatics, Harvard University

Dr. John Quakenbush is a professor of Computational Biology and Bioinformatics in the Biostatistics Department at the Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute, as well as the director of the Center for Cancer Computational Biology (CCCB). His research group focuses on methods spanning the laboratory to the laptop that are designed to use genomic and computational approaches to reveal the underlying biology. In particular, they have been looking at patterns of gene expression in cancer with the goal of elucidating the networks and pathways that are fundamental in the development and progression of the disease.

Lorenzo Trippa

Lorenzo Trippa

Associate Professor of Biostatistics, Harvard University

Dr. Lorenzo Trippa is an associate professor of Biostatistics in the Biostatistics Department at the Harvard T.H. Chan School of Public Health. He is interested in the development of adaptive clinical trial designs, and his research includes the study of algorithms and methodologies for the analysis of data generated by adaptive trials. He is also particularly interested in Bayesian nonparametrics, a great source of modeling opportunity in biomedical applications as well as both computational and theoretical problems.

Christine Choirat

Christine Choirat

Research Scientist, Harvard University

Dr. Christine Choirat is a research scientist at the Institute for Quantitative Social Science (IQSS) and research scientist in the Biostatistics Department at the Harvard T.H. Chan School of Public Health. Christine was trained as a statistician and has over twenty journal and conference publications. Her research focuses on quantitative methods applied to economics, management, decision theory and psychology, with a special emphasis on experimental approaches. She is particularly interested in issues related with governance and ethics.

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