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This introductory course into Open Data (Open Data Basics) familiarizes students with current topics on (open) research data and will introduce to them important aspects and components of research data management and the data lifecycle. The course will also teach students about data repositories, including how to find existing data from such repositories, and about some of the legal aspects of data reuse and sharing, e.g. data licenses. The introductory course is intended for bachelor students, master students and also PhD students who have little or no prior knowledge of research data management. The course takes place as a 1-day face-to-face event with online learning components before and after the course. The course takes place as a 1-day face-to-face event with online learning components before and after the course. Preparatory tasks (on OLAT) need to be completed before the course date and take up to 8 hours to complete.
After this course, students are able to:
This introductory course is intended for Bachelor's, Master's and PhD students who have little or no prior knowledge of research data management. This course might not be suitable for first-year BA students; please contact the course administrator (Melanie Röthlisberger) to discuss your case.
Wednesday, 22.03.2023 (09.00 - 17.00)
The course will be held in English.
Every semester
The final assessment consists of a written description of a dataset, peer feedback and a reflection on discipline-specific similarities/differences. / 1 ECTS
In the course Working with FAIR data, students are familiarized with important aspects of FAIR (findable, accessible, interoperable, reusable) data, which is an essential prerequisite for publishing and sharing data and for increased reproducibility and replicability of scientific research. Students will learn how to make their own data FAIR following existing standards (e.g. metadata) and how to assess the FAIRness of existing datasets. The course takes the form of a one-day face-to-face event with online learning components before and after the course.
The course is intended for interested Bachelor's, Master's and PhD students who have prior knowledge in data management and would like to share their data with the scientific community and increase its reusability.
Wednesday, 19.04.2023 (09.00-17.00)
The course will be held in English.
Every semester
The final assessment consists of a written documentation of a dataset (incl. assessment of its FAIRness and improved metadata documentation), peer feedback and a reflection on discipline-specific similarities/differences. / 1 ECTS
In the course Data Management Planning, students are familiarized with important aspects of data management planning along the data life cycle, i.e. data documentation and organization, data processing, data storage, data preservation and protection, archiving and publishing as well as data reuse and discovery. Legal aspects of data sharing and reuse as well as ethical aspects of data management (e.g. working with personal data) are also covered. The course aims to equip students with the necessary knowledge to write their own data management plan (DMP) according to the funding requirements (e.g. SNSF). The course takes place as a one-day face-to-face event with online learning components before and after the course.
The course is intended for Master's and PhD students as well as interested Bachelor's students who have little or no prior knowledge of data management planning. It is recommended that participants will or are about to write a data management plan for a future research project.
Wedneday, 17.05.2023 (09.00 - 17.00)
The course will be held in English.
Every semester
The final assessment consists of a preliminary version of a DMP, peer feedback and a reflection on discipline-specific similarities/differences. / 1 ECTS
The course Publishing personal and sensitive data familiarizes students with the legal aspects when sharing and/or publishing data containing personal and/or sensitive information of study participants. Students will learn about copyright, licenses, data protection, disclosure risk and data utility and will practice reproducible anonymisation techniques in hands-on sessions (for qualitative and quantitative data). This course is intended for master and PhD students and interested bachelor students working with personal and/or sensitive data. Participants working with quantitative data are required to have some prior working knowledge of R.
The course will be held in cooperation with the Center for Reproducible Science. It takes place in two half-days with online learning components before and after the course.
After this course, students are able to 1) understand the principles of data protection and copyright, 2) distinguish and select appropriately between the different types of licenses to publish their work, 3) characterize sensitive/personal data and practice the sharing of such data in some cases, 4) describe the difference between pseudonymization vs anonymization, 5) understand the trade-off between disclosure risk and data utility, 6) practice some easy techniques in R (e.g. re-coding, suppression, aggregation) and are able to 7) apply methods for statistical disclosure control.
This course is intended for Master's and PhD students and interested Bachelor's students working with personal and/or sensitive data. Participants working with quantitative data are required to have some prior working knowledge of R.
Thursday, 20.04.2023 (13.00 - 17.00)
Thursday, 04.05.2023 (13.00 - 17.00)
The course will be held in English.
Every semester
The assessment consists of a portfolio with two tasks. The first task requires students to work on a case study. For the second task, students will be required to hand in a script in R (for quantitative data) or a description of their anonymisation technique (for qualitative data) for a specific given dataset. The final deadline for the portfolio is due two weeks after the last course day (18 May 2023). / 1 ECTS
Der Kurs will be held in collaboration with the Center for Reproducible Science.
The introductory course Open Access Basics introduces students to the field of Open Access and Open Science. They learn about the latest developments in the publishing landscape, explore the characteristics of Open Access and the differences to publications in traditional, subscription-based journals. Publication versions and types will be discussed as well as challenges (young) researchers face with regard to Open Access publishing. The course takes the form of a one-day face-to-face event with online learning components before and after the course.
This introductory course is intended for Bachelor's, Master's and PhD students, with no or little prior knowledge of Open Access. This introductory course is intended for Bachelor's, Master's and PhD students who have little or no prior knowledge of research data management. This course might not be suitable for first-year BA students; please contact the course administrator (Melanie Röthlisberger) to discuss your case.
Wednesday, 23.03.2023 (09.00 - 17.00)
The course will be held in English.
Every semester
A draft version of the final assessment is due 5 days after the day event, the peer feedback is due 6 days later and the final submission is due 3 days later (in total two weeks after the day event). / 1 ECTS
If you need background knowledge before attempting a module which contains advanced topics for you or if you want to deepen or broaden your knowledge in a specific direction you can combine the Open Data / Open Access Modules with Modules on Open and Reproducible Science, also offered at the School for Transdisciplinary Studies.
We suggest three combinations totalling 3 ECTS, these specific combinations logically fit together in a theme but all other module combinations are allowed as well.
