In the open science movement, research data is expected to be as open as possible and as closed as necessary. But what does this mean in practice, especially when dealing with patents, personal data or other sensitive information? This lecture will present the principles of open and FAIR data, research data management and the legal, ethical and institutional frameworks that govern data sharing. Participants will gain an overview of researchers’ rights and obligations and funders’ requirements, as well as guidance on data repositories.

In this Basic Statistics Course you will learn the basics of statistical data analysis for your research projects. The course is application based and will empower you to improve your study planning and get more confident with your statistical analysis.

It contains the following topics:
Design of experiments / study design
Data collection and entry
Structure of statistical analysis
Significance tests
Choosing the appropriate statistical method
Paired and unpaired data
Level of measurement / variable type
Checking of assumptions and normality
Effect size measures
Sample size calculation and power analysis

The course is organized with self-learning material (short videos, handout and exercises) and two live and online Q&A sessions. After a short kick-off meeting you watch the videos and do the exercises at your own pace. During this time, you can pose your questions in a chat. Within an interval of one week we meet for two Q&A sessions online, where you can ask questions concerning the topics or your own projects.

The course runs for just over two weeks. Plan approx. 2.5 hours per week for independent work with the materials plus the time for the meetings (30 minutes kick-off and 2x 45 minutes Q&A session).

Collecting, processing and analyzing data are central activities for virtually every researcher. Topics like data sharing and data publication are becoming increasingly important. Nevertheless, many research projects lack a structured and well-organized data management. This course is meant to give a general, discipline-independent introduction into various topics central to an efficient management of research data with a special focus on questions related to data archiving and data sharing. Both are central aspects of good scientific practice. Archiving and long-term preservation of research data are prerequisites for the scrutiny of scientific results based on the analysis of this data. Data sharing on the other hand increases transparency of research results and enables possible re-usage of data for new research questions, in combination with additional data sets and in interdisciplinary contexts.

In particular, the course will cover the following topics:
- Requirements on research data handling from universities, research funders and scientific journals
- Short-term and long-term preservation: formats, metadata, documentation, standards
- Open Research data, data publication and data citations: Where? How? Why and why not?
- Creation of data management plans for research projects