In this track, early career researchers who are Open Science novices learn to make their research more transparent, reproducible and credible in the eyes of their peers, the public, and funding agencies. This track consists of public lectures accessible to anyone as well as hands-on practical workshops for selected applicants. Participants of this track learn how to: Specify research design and set up statistical plans in advance of collecting data to mitigate biases in analyses, with the help of preregistration and data simulation - Create computationally reproducible workflows to be more efficient and spot mistakes in data wrangling or analyses, through programming with version controlled scripts - Prepare and share data and code in connection with manuscripts by applying the Findable, Accessible, Interoperable, Reusable principles, and using adequate repositories and licenses to accumulate credit and citations.

In this track, early career researchers who are Open Science novices learn to make their research more transparent, reproducible and credible in the eyes of their peers, the public, and funding agencies. This track consists of public lectures accessible to anyone as well as hands-on practical workshops for selected applicants. Participants of this track learn how to: Specify research design and set up statistical plans in advance of collecting data to mitigate biases in analyses, with the help of preregistration and data simulation - Create computationally reproducible workflows to be more efficient and spot mistakes in data wrangling or analyses, through programming with version controlled scripts - Prepare and share data and code in connection with manuscripts by applying the Findable, Accessible, Interoperable, Reusable principles, and using adequate repositories and licenses to accumulate credit and citations.