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Including data literacy in teaching and continuing education

Excerpt from "Guidelines on Digital Research Data at TU Darmstadt"

"Data literacy is a prerequisite for efficient and sustainable handling of research data and for compliance with good scientific practice. The methods of subject-specific research data management should therefore be appropriately anchored in teaching and continuing education. Due to the outstanding importance of today’s students for tomorrow’s science, the TU Darmstadt advocates the idea of "good research data management from the very beginning" and, in addition to teaching the theoretical basics at an early stage, strives for the continuous and practical application of the methods and tools in student internships and thesis projects." (Guideline 8)

Whenever you give lectures or organize practical courses, please also include the required data literacy in the topics you cover. Make sure you demonstrate the importance and benefits of efficient data handling and discuss the relevance of FAIR as well as open data in your discipline. Please take into account the learning objectives matrix from DINI/nestor AG Forschungsdaten and other material provided by different sources (see below). Among others, the following learning goals should be covered:

  • technical skills, e.g.
    • structured digital documentation of data
    • creating interoperable digital data and metadata
    • automatisation
      • generation of data and metadata
      • making use of structured and consistent metadata
    • preservation and archival of data
    • suitable tools, e.g. ELNs, git
  • conventions
    • file and data formats
    • terminology and standards
    • best practices
  • theoretical background, e.g.
    • open science
    • data science

In addition, please make sure that people in your working group possess an adequate level of data literacy for their tasks and give them the opportunity to develop their skills in that area through continuing education.

Formats and examples

If you need inspiration, we have collected a number of examples of successful integration of data literacy in practical courses as well as learning resources that you can reuse in your lectures.

Examples of practical courses

Examples of learning resources