When publishing in a scientific journal, you often have the choice to make your findings available freely, openly accessible for the entire world. Additionally, a standard requirement is that all of the source data underlying your findings are openly shared using the FAIR ('Findable, Accessible, Interoperable, and Reusable') data principles. In Systems Biology research, these datasets are generally huge. Hence, next to proper experimental design, data management has become of utmost importance in Systems Biology research. This course covers all aspects of study design and data management, including scientific data analysis and the FAIR data principles. Additionally, you will learn how to perform basic as well as advanced data analyses in the scientific programming language R. The R skills trainings within the course are supported by DataCamp. DataCamp offers interactive, online courses in R and Python for data scientists and students. For more information, please visit: https://www.datacamp.com/
* Learn to explain important aspects of scientific data management, including data analysis, data stewardship, data archiving and data sharing; * Learn to distinguish between the relative merits and use cases for the diversity of study designs used in the field of Systems Biology research; * Learn to explain the principles of FAIR data sharing and open-access publishing; * Learn to perform analyses on biomedical research data in the statistical programming language R; * Learn to critically review research quality and methodology as they are used in daily practice, with a focus on study design, open-access and adherence to the FAIR data principles.
Literature will be provided at the start of the course.