This course will guide participants towards a reproducible research workflow supported by R. It will provide participants with knowledge how to prepare their data and analyses so that other researchers can understand (and eventually reproduce) how figures and statistical tests were created stepwise from the raw data. The course is in two blocks. N introductory course (3 sessions) and an advanced part (8 sessions). It is possible only to book the intro course or the advanced course or both. We will use R (and RStudio) as interpreted programming language. The course will blend on-line lessons on DataCamp and custom made exercises with weekly 2 h hands on sessions. This course is generously supported by DataCamp by providing free access to online learning material for R for our course.
- comprehend the importance of reproducible research.
- learn data analysis in R through the tidy approach.
- be able to create data visualisation via ggplot2.
- understand the process towards a fully reproducible analysis notebook.
- receive an introduction to linear and mixed models.
- be able to do Preregistration and understand registered reports.
- learn how to write a data management plan and receive information on open data repositories.
- understand the usefulness of metadata.
- receive an introduction to open access publishing models contrasted with traditional models.
Dr. Ulf Tölch, QUEST Center for Transforming Biomedical Research, Berlin Institute of Health
Meggie Danziger, QUEST Center for Transforming Biomedical Research, Berlin Institute of Health