Description
This contribution presents a practical use case of embedded data stewardship within the ongoing MultiplEYE project—a large-scale, collaborative COST Action that aims to create a standardized, multilingual eye-tracking-while-reading dataset across more than 30 countries and 35 languages. As a Data Steward from the Leibniz Institute for Psychology (ZPID), I am embedded in the project’s core team and support the implementation of FAIR principles throughout the entire research data lifecycle.
My role bridges infrastructure and research perspectives: I work closely with the researchers generating the data, while also coordinating with the infrastructure team responsible for building EyeStore, a FAIR-compliant repository hosted by ZPID and tailored to the long-term publication of eye-tracking data.
The contribution visualizes how FAIR principles are put into practice within each phase of the data workflow—from planning and standardized data collection to metadata documentation, quality reporting, and publication. Specific examples include the metadata schema, processing stages, and the role of documentation in supporting data transparency and reuse. It offers a real-world perspective on how an embedded data steward can help align the needs of researchers and infrastructure providers, promote standardization, and facilitate sustainable open data practices in complex, cross-institutional research settings.
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