Organisers

• Silvia Di Giorgio (ZB MED – Information Centre for Life Sciences)
• Alan O'Cais (University of Barcelona)
• Ignacio Pagonabarraga (University of Barcelona)

Trainers

• Alan O'Cais (University of Barcelona)
• Lisanna Paladin (EMBL - Heidelberg)
• Sabry Razick (University of Oslo)
• Rabea Müller (ZB MED - Information Centre for Life Sciences)

BioNT - BIO Network for Training - is an international consortium of academic entities and small and medium-sized enterprises (SMEs). BioNT is dedicated to providing a comprehensive training program and fostering a community for digital skills relevant to the biotechnology industry and biomedical sector. With a curriculum tailored for both beginners and advanced professionals, BioNT aims to equip individuals with the necessary expertise in handling, processing, and visualising biological data, as well as utilising computational biology tools. Leveraging the consortium's strong background in digital literacy training and extensive network of collaborations, BioNT is poised to professionalise life sciences data management, processing, and analysis skills.

This intensive three-day workshop focuses on collaborative and FAIR software development practices critical for data scientists. Participants will engage in hands-on learning across three key domains:

• Day 1 is dedicated to collaborative distributed version control, providing practical training on collaborative technologies. Participants will explore collaboration concepts, repository management, code review practices, and software licensing.
• Day 2 begins a practical exploration of the concepts introduced for the case of a Python project. It covers the tools we can use to implement FAIR principles, how to create a reproducible environment, tips to make code readable, and how to structure your application.
• Day 3 concentrates on software testing, automated testing strategies, how to document code, how to facilitate code citation, and collaborating with others.

The workshop offers a comprehensive, hands-on approach to developing professional software engineering skills tailored to data science research.

Join this workshop if you are:

• Interested in advancing your collaborative software development skills
• Working on computational projects that require version control
• Seeking to improve your code review and contribution techniques
• Wanting to learn professional software licensing practices
• Interested in implementing robust software testing methodologies
• Committed to creating reproducible research environments
• Looking to systematically manage computational projects
• Eager to learn best practices in dependency and environment management
• A student, researcher, or professional in data science
• Aiming to enhance your software engineering capabilities

Learning Outcomes: 

By the end of this workshop, you will be able to:

1. Implement collaborative distributed version control techniques
2. Understand and practice collaborative workflows
3. Conduct effective code reviews
4. Contribute to repositories owned by others
5. Navigate software licensing considerations
6. Design and implement local software testing
7. Create automated testing strategies
8. Develop reproducible research environments
9. Manage software project lifecycles
10. Systematically organize computational projects
11. Record and manage computational dependencies and environments