Content and Objectives

Research data, as valuable research results, require professional management. Comprehensive research data management is therefore becoming increasingly important. This is what data stewards are responsible for. The role is being established not only in the business world but also at research institutions in Austria and abroad as international best practice in research data management.

Data stewards operate at the interface between scientists and scholars, and research infrastructure and help to bridge the gap between these two areas. They support researchers in processing data in a sustainable way and carry out needs assessments and requirements engineering.This certificate program will help the participants to acquire knowledge, expertise and key competences to perform tasks as data stewards in research institutions.

This continuing education program links the latest findings on research data management, open science and open research with the tasks of data stewards.



  • 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
    •     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

Qualification profile

Successful participants have profound knowledge of ways to develop innovative services in the field of research data management and increase awareness of open science. They are able to systematically assess researchers’ needs and offer tailored support. Together with researchers and software developers, they design innovative workflows for FAIR data management. The acquired competences enable the participants to fulfil the forward-looking role of data stewards at various research institutions.