PhD course: Hands-on Training on Open and Reproducible Science, 2 ECTS

ECTS: 2 points (approved as a BSS PhD course)

Teachers
Stefan Pfattheicher (PSY) coordinator, Jesper Wiborg Schneider (PS), Panos Mitkidis (MGMT), Karolina Ścigała (PSY), Christian Truelsen Elbæk (MGMT), Janis Zickfeld (MGMT), Daniele Nosenzo (ECON), Sergio Pirla (PSY), Kristoffer Ibsen (ECON), Yngwie Asbjørn Nielsen (PSY).

Course content
Research following open science principles produces more reliable results, is more trusted by readers, and an increasing number of top journals now require adherence to open science principles. There is no doubt that open science principles are becoming more and more important.

This course is designed to equip PhD students with the necessary skills to navigate the open science space. It is a hands-on course, meaning that PhD students will learn how to apply open science principles to their research projects. The course is relevant for those using experimental methods, correlational designs, meta-analyses, or working with large data sets (e.g., registered-based research). The course is designed for, but not restricted to, PhD students in the social, behavioural, and health sciences.

Particular attention will be paid to sample size justification (i.e., power analysis), pre-registration, meta-analysis, registered reports, and data sharing and management. The course will also provide an opportunity for students to ask questions, exchange perspectives and concerns, and discuss practices and current developments. We will begin by discussing the advantages and necessity of following open science principles, considering different practices in different disciplines. We then discuss meta-analyses from an open science perspective, followed by how to do a power analysis and how to pre-register research. Challenges with power-analyses and pre-registrations will be discussed, as well as the opportunity offered by more and more journals: registered reports. We will learn the dos and don'ts of data management, and how to write a paper following open science principles. As a take-home assignment, each student will write a pre-registration (e.g., for one of their empirical studies), and will get feedback on it.

The course aims to provide

1. Understanding the advantages and importance of following open science practices for one’s own research as well as science as a whole.
2. Concrete tools and a practical introduction to open science practices: pre-registration, registered reports, power analysis, meta-analysis and bias detection, and data management and sharing.
3. Know-how for including open science practices in one’s own research.
4. An opportunity to get feedback on a pre-registration and a power analysis of one’s own project.
5. Discussion about challenges, concerns, and limitations of open science practices.

Literature
Pre-course preparation includes reading the course curriculum. Relevant texts will be made available to participants in advance, helping to enhance their understanding throughout the course.

Evaluation
Requirements for receiving course credits are: First, active participation on all 3 days of the course. Second, the completion of a home assignment at the end of the course: Each student will write a pre-registration for one of their empirical studies or will evaluate an existing pre-registration from a published paper (max. four standard pages). Students will get feedback on their home assignments.

Target group: PhD students from all faculties and universities are welcome.

Deadline for applying: 28 September 2023.

Maximal number of participants: 20.

Applying for the course: Please complete the online application form.