Open and Reproducible Science

The aim of this course is to equip PhD students with the knowledge, skills, and practical experience needed to make their research transparent, reusable, and reproducible. By engaging with concepts of open science, data management, version control, and peer review, students learn how to prepare, document, and share their own datasets and analysis workflows in line with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The course supports students in building robust open-science practices that will help to enhance the credibility, impact, and collaborative potential of their research.

1. Lectures on open science aspects
   • Introduction into open and reproducible science (importance, ethical aspects, some case studies, FAIR principles, etc.)
   • Lecture and exercise on how to write a data management plan
   • Tools for open science (e.g. lecture and exercise on GitHub/version control)
   • Lecture and exercise on how to do a peer-review
   • Introduction to open data repositories (mainly the Bolin Centre Database but also more general repositories)
     
2. Writing of a data descriptor (main task) and preparation of own field/laboratory data and analysis code for the Bolin Data Centre (or other relevant database)
   
3. Review and reproducing of an existing dataset ideally from a fellow class-mate (this will help to see that their and their peers work is really reproducible)
4. Presentation of the open data product / descriptor or writing of a short report for the Bolin Centre website (or social media)

The course is planned to run continuously and will give 7.5 HP.

After the finalization of the course, the students are expected to (Individual Learning Outcomes):

1. Explain the core principles of open science (including its ethical dimensions and societal implications)
2. Apply data management strategies (including metadata creation and data documentation, to prepare datasets for sharing)
3. Implement reproducible research practices using tools for version control, scripting, and computational workflows
4. Be able to use collaborative platforms (e.g., GitHub, Zenodo) to share research outputs and engage with scientific communities
5. Critically assess open science practices in peer-reviewed publications and research proposals
6. Design and carry out a small-scale research project that follows open and reproducible science principles (including data and code sharing)
   

The course will run continuously, the students will need approx. 6 months for completing the course. NB: *Tentative dates (we can adapt if needed with times).

Students need to attend the dedicated lectures, develop and present their data descriptor, review their peer’s data descriptor and present their final product in a joint seminar. The final report (data descriptor and uploaded data) and the final presentation (20 min + Q/A) will be examined. Each will count equally (50/50) and the final grade will be pass or fail. In the report, students need to show that their data is properly described and reproducible. In the oral exam (final presentation), the core principles of open science and specifics about their data project will be questioned. The examiner will be the course leader together with one course assistant or teacher.

To register for the course, please fill out the following survey (deadline 30 March 2026): https://forms.gle/8tcCpdhWfKA2pELJ8

Please contact the course coordinator in case you have questions about the suitability of your data and the participation in this course.

Course coordinator

Paul Zieger, ACES (paul.zieger@aces.su.se)