Contents in Detail

Module 1: Basics of Research Data Management and Open Science

Introduction to:

  • Research data management
  • Good scientific practice
  • Open Access, Open Science, Open Data
  • Cost estimation and financing models
  • Legal and ethical aspects
  • Role and Tasks of Data Stewards

Module 2: Basics of IT and Data Science

Introduction to:

  • Data science and data-driven research
  • Machine learning
  • Database systems

Basics of programming (based on the Carpentries curriculum):

  • Unix Shell
  • Git and GitHub
  • Working with Python

Module 3: FAIR Research Data in the Life Cycle 

Research data life cycle:

  • Project management and funding landscape
  • Data management plans (DMPs)
  • Data organization and curation
  • Data visualization
  • Metadata and research data documentation (incl. persistent identifiers, ontologies, etc.)
  • Data security and storage
  • Repository management and long-term preservation
  • Interoperability and data migration
  • Data reuse (incl. legal requirements)

Discipline-specific approaches to data stewardship in the:

  • Natural and life sciences
  • Humanities
  • Social sciences
  • Technical sciences

Module 4: Research Data Management Support 

  • Developing research data management support services
  • Designing and delivering training
  • Conducting needs-assessments and requirements engineering

Modul 5: Data Stewardship in Practice: Project Work

  • Individual or group project applying the acquired skills and knowledge to data stewardship practice