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Research Data

Research data is basically all data (digital and analog), which you use or generate for a research project. Data can also be one of the results of research. The definition and understanding of research data differs depending on the scientific discipline.

What is FAIR data?

One of the aims of Research Data Management is to securely store and provide access to data necessary to replicate the results, and their reuse. The FAIR Data Principles were developed to support these goals. FAIR describes how researchers should handle research data and other important information about the data (metadata). FAIR is used by many organisations such as the European Commission as a standard of Research Data Management.

Findable – Data and metadata should be easy to find by both people and machines. Basic machine-readable and descriptive metadata enables the discovery of interesting datasets.

Accessible – Data and metadata should be archived and made available in such a way that allows easy retrieval and download by machines and people.

Interoperable – Data should be available in such a way that humans and machines can exchange, interpret and combine these with other data sets in a (semi-)automated way.

Reusable – A good description of data (metadata) ensures that it can be reused for future research and is comparable to other data sources. Data should be permanently citable (e.g. with a DOI).

FAIR data principles for research data

The TIB blog (Leibniz Information Centre for Science and Technology and University Library) provides a detailed description of the FAIR data principles for research data including a checklist how to make your research data FAIR!

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