Reproducible Research

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The main goal of the course is to present the basic concepts of reproducibility and repeatability in research, their significance in scientific and commercial research and development processes, and to impart to students basic practical knowledge about several of the most popular modern tools for reproducibility in the industry. Upon completion of the course, students will understand the general concept of research reproducibility and comprehend which tool can be used in a given context. They will also acquire skills in using computer tools that enable achieving reproducibility and repeatability of research, and they will be able to apply the skills acquired during the course in participating in modern scientific and commercial data science projects.

The course consists of computer labs, with classes including a theoretical and practical part. The following topic blocks 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 code testing

6. Introduction to online repositories

7. Introduction to metaanalyses

Lecture slides or notebooks

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 and its presentation (in teams)