Making FAIR Practices Accessible and Attractive

Abstract

Facing rapidly growing volumes of research datasets, scientists and research funding agencies are putting forward new principles of data management, such as data Findability, Accessibility, Interoperability, and Reusability (FAIR). To this end, data science experts are developing FAIR data policies, methods, protocols, and repositories, while actual research practices are lagging behind because FAIR compliance remains a burden for many researchers. Here we present a prototype data management infrastructure deployed at the National High Magnetic Field Laboratory (NHMFL/MagLab) aimed at helping scientists efficiently annotate and manage experimental data produced by their MagLab projects and making FAIR practices accessible and attractive. The infrastructure incorporates the Open Science Framework (OSF) data repository platform. We will describe infrastructure elements such as the data formats, the metadata schema, the repository integration, the naming conventions, the templates to organize the data, and the automated data pipeline from measurement stations to the FAIR repository objects.