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Data production

Research data management starts with collecting data.

Systematic research data management should not only focus on archiving and accessing data. It must also always take the process of data collection into account. The documentation and quality assurance of research data is particularly challenging in the social, behavioural, educational, and economic sciences for various reasons. First, empirical research utilises a very broad spectrum of data types, including both structured data, e.g., data collected in surveys, and unstructured data such as video, audio, and textual data. Secondly, the above-mentioned research disciplines traditionally use data generated outside of science, e.g., administrative data or, more recently, observational, behavioural, and tracking data. In the latter case, the methodological quality is less transparent and often requires additional steps to render the data useful for science. This field of action therefore aims at further increasing the quality of data production by establishing and continuously improving RDM in the social, behavioural, educational, and economic sciences.

Harmonised Variables – Combining survey data more easily through standardised and harmonised variables

Unlocking the potential of survey data from multiple sources by achieving their interoperability.

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Linking textual data

Linking textual data with other types of data.

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CODI – A service for coding open responses in surveys

Automatically and efficiently coding text responses to open-ended questions into standardised, quality assured categories.

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Open data format

Open, non-proprietary format compatible with common statistical programs for sharing research data and metadata.

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Supporting research data centres

Creating benchmark processes and quality standards to support RDCs in implementing FAIR principles for sustainable and trustworthy data access.

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