From Bench to Brain: A Metadata-driven Approach to Research Data Management in a Collaborative Neuroscientific Research Center - Marlene Pacharra; Tobias Otto; Nina Olivia Caroline Winter (Data Science Journal)
Research data management (RDM) is key to fast and effective cooperation in collaborative research projects, especially when several scientific disciplines are involved. Critical steps in RDM are the agreement and implementation of common standards for metadata and data storage. The practice paper outlines the approach of the interdisciplinary Collaborative Research Center (CRC) 1280 ‘Extinction Learning’ with 81 researchers from biology, psychology, medicine, and computational neuroscience across four different institutions. This framework can serve as a transferable model for other collaborative research initiatives lacking predefined metadata schemas or repositories. Specifically, the iterative process of agreeing on 16 metadata fields that correspond most highly with the involved research disciplines for collaboration is discussed. To increase reusability, mappings to the Dublin Core and DataCite bibliometric standards were established for descriptive metadata fields. Moreover, we deploy controlled vocabularies and terminology tailored to the respective disciplines and to the organizational structure of the CRC. Using this metadata schema, neuroscientific data from more than 3,200 human subjects and lab animals are currently shared within the CRC. To enable implementation of this schema in active research, open-source applications have been developed that store metadata as local JSON files along with research data and make metadata searchable. The development of the metadata schema and its subsequent use are illustrated with particular emphasis on design principles that allow for reuse in other RDM domains and the lessons learned.