Metadata

Metadata is a set of data describing some other data. For example, for a simple thermometer measuring water temperature, metadata information are its location, the time of measurement, the medium in which it is measuring temperature, the manufacturer, the scientists taking care of it, etc. In contrast, the data of the thermometer could be a measurement of 5°C. Even the unit of °C is metadata information.

Without metadata, any data cannot be evaluated, discussed and reused, because the context of the data is missing. Going back to our example, the data point “5”, without knowing that it is in the unit of °C and a measurement of water temperature of the Elbe River in Geesthacht on January 1st, 2025, at 10:00am is not usable. For a numerical model used in the study of climate change, historical data is necessary. The historical data can only be reused though if enough metadata is available describing in which context the data was gathered. Storing as much metadata as possible with its corresponding data is thus of utmost importance.

We support the scientists at Hereon with curating their metadata of their data. We implement novel concepts and technologies to ensure high-quality metadata. For example, we develop metadata guidelines in collaboration with other Helmholtz Centers to ensure standardization across the research discipline. We also provide technical equipment to facilitate metadata collection in the field, where sampling takes place. Our main goal while improving the quality of metadata is to ensure that all of our research data is FAIR.

Metadata enhances our research efficiency by providing structured, searchable information about data, such as its source, context, and format. This enables researchers and anyone else interested in the data to quickly locate, access, and understand relevant datasets. Metadata also fosters collaboration by standardizing data descriptions, ensuring compatibility across platforms and disciplines. Standardized metadata helps with streamlining FAIR data management which in turn accelerates analysis of already existing datasets, facilitates reproducibility, and maximizes the impact of research efforts.