This course is an entry-level introduction to the fundamentals of scientific metadata for PhD students, early-career researchers, and postdocs. In this course we will look at the intricate relationship between (digital) research data, metadata, and knowledge; discuss why metadata is critical in today’s research; and explain some of the technologies and concepts related to structured machine-readable metadata.

Have you ever struggled to make sense of scientific data provided by a collaborator? Or, even worse, to understand your own data five months after publication... Do you have difficulties in meeting the data description requirements of your funding agency? Do you want your data to have lasting value; but don’t know how to ensure that?

Precise and structured descriptions of research data are key for scientific exchange and progress - and for ensuring recognition of your effort in data collection. The solution: make your data findable, accessible, interoperable, and reusable - by describing them with metadata.

This course is targeted especially at scientific staff and researchers in the Helmholtz Research Field Matter but is open to anyone who would like to better understand research data annotation with metadata.

What You Will Learn:

• Understanding the vital differences between data and metadata.
• Techniques for annotating research data using structured metadata.
• Identifying and implementing appropriate metadata frameworks and data repositories.
• Developing basic skills in Markdown, JSON, XML.
• Exploring tools to improve your metadata annotation capabilities.
• Recognizing the role of structured metadata in enhancing your scientific visibility.

In this course, we will specifically focus on matter-related datasets, formats, and metadata schemas. However, it is open to anyone interested in learning about structured metadata and its importance in scientific research.