Reproducible Research

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The main objective of the course is to present the key concepts of the research reproducibility, its importance in scientific and commercial R&D processes, and to provide students with the basic practical knowledge of a few most popular in the industry modern reproducibility tools.

The course consists of computer labs, with classes including a theoretical and practical part. The following topics will be discussed (not necessarily in the presented order):

1.Introduction:

The three Rs: Repetition, Reproduction, Replication;

Importance of reproducibility in science and the R&D process;

Reasons for and consequences of lack of reproducibility;

Some ways of handling non-reproducible research;

Course grading overview.

2. Version control systems:

Introduction to VCSs and git;

Using git for version control and progress documentation;

Teamwork via git;

Working with GitHub;

Project workflow;

GitHub as the course repository and as ‘home’ for final projects.

3. Reporting tools:

Introduction to Quarto and Markdown;

Reproducible and automated reports;

Reports with data inputs;

Other formats

4. Writing reproducible code:

Documenting code and versioning;

Tools for managing software versions;

Principles of writing clean and clear code.

5. Introduction to online repositories

6. Introduction to metaanalyses

7. Introduction to cloud computing: introduction to the tools and practices allowing the usage of remote computers for calculations, automating work

8. Introduction to the Linux shell

Lecture slides

Numerous online resources

Upon the completion of the course, student:

1. understands the general concept of research reproducibility; knows the reproducibility tools classification; understands which tool can be used in a given context;

2. has basic skills in computer tools allowing to achieve research reproducibility and replicability; has basic skills in modern best programming practices; has basic skills in the cloud development environment; is able to employ skills gained during the course while participating in modern scientific and commercial data science projects;

is aware of the importance of reproducibility in data science, as well as in science and development in general; is aware that reproducibility tools are evolving rapidly and that constant training in this area is required to keep skills up to date; is aware of the trends in modern data science and IT development;

1. Active participation in the classes

2. Final project (in teams)