Open and Reproducible Science

Center for Reproducible Science

Open Science Team

Description

The course is divided in six topics:

1. Introduction to Open science and its relation to scientific integrity and reproducible research
2. Practical guidelines on how to organize data and projects in view of reproducibility.
3. Definition of quality criteria for good research when critically appraising publications and a discussion how these criteria are related to transparency and reproducible research
4. Introduction to some tools for scientific collaboration
5. Introduction to reproducible notebooks for data analysis
6. Implementation of the learned principles of Open Science and reproducible research when visualizing data

These topics are taught in seven two-hour in person training meetings with digital input and homework in between. In this flipped classroom students are required to learn about concepts at their own pace using provided video and reading material and complete tasks before an in-person session. Assignments and the in-person session contain peer and staff feedback and assessment.

Target group

Students of all disciplines which are working at least in part empirically and who have had an introduction to empirical research. Intermediate IT skills are a prerequisite (students need to be able to install packages and programs and they need to know the basics of R).

Course dates

Tuesday 16.00 - 18.00

22.02.2022 - 05.04.2022

Assessment / ECTS Credits

Portfolio assessment: 70% of all input, homework and in-class have to be passed to receive the credit point.

1 ECTS

Description

The course is divided in five topics:

1. Version control
2. Questionable Research Practices
3. Reproducible computing
4. Good statistical practice
5. Tools in R for data and meta data handling

These topics are taught in seven two-hour in person training meetings with digital input and homework in between. In this flipped classroom students are required to learn about concepts using provided video and reading material and complete tasks before an in-person session. Assignments and the in-person session contain peer and staff feedback and assessment.

Target group

Students of all disciplines which work at least in part empirically. The participants have gained first experience with research, are active users of the scientific literature and had an introduction to statistics. Good computer knowledge is expected including experience in R.

Course dates

Tuesday 16.00 - 18.00

12.04.2022 - 31.05.2022

Assessment / ECTS Credits

Portfolio assessment: 70% of all input tasks, homework and classroom tasks must be solved to receive the credit point.

1 ECTS