After successful completion of the course, students are able to:

• explain the research & data lifecycles
• review existing Data Management (DM) practices
• create a Data Management Plan (DMP)
• differentiate between different software and data license types
• explain what persistent identifiers are (e.g. DOI)
• apply FAIR principles in practice
• explain how findability and interoperability of data can be achieved
• understand the function of metadata and standards for efficient (research) data management
• describe components of a Research Data Management (RDM) infrastructure
• discuss differences between repository systems
• describe the scope of RDM policies and how they drive DM activities and obligations
• explain challenges in digital preservation
• plan, assess, and implement reproducible experiments
• understand and apply Open Science principles

• Reproducible Research
• Metadata & Data Packaging
• Machine-actionable Data Management Plans
• Research Data Management Services
• Repository Systems
• Data Identification and Citation
• Digital Preservation Challenges, Preservation Actions
• Preservation Planning & OAIS Standard
• FAIR principles
• Legal & Ethical Aspects of Research Data Management

Lectures, (group) exercises, and self-study (reading materials, videos, etc)

Check TUWEL for the exact schedule.

A set of assignments must be solved to pass this course. Assignments may include solving challenges in the management of data applying the recommendations and guidelines from the lecture, using domain specific tools and services for data management, and a subsequent cap presentation.