FAIRBiRDS

Transitioning towards FAIR data has been notoriously challenging in the long tail of science. This is mainly because the long tail consists of many independently assembled datasets different in structure and management, yet collected with a similar purpose. This proposal builds on the recently established SPI-Birds Network and Database on long-term population studies of birds, hosting and standardising individual-level data. Our aim is to develop this network as a model for other (ecological) research domains, with particular emphasis on the currently under-addressed stages of data processing, data analysis, and data preservation. First, we will develop a repository with citable datasets that can be linked to publications, analytical codes, and other output, thereby closing the research lifecycle in the long tail of science. Second, we will expand the user base with respect to both data producers (expanding geographic and ecological coverage of the datasets), researchers within and outside the SPI-Birds community, and users outside research such as educators, journalists, environmental professionals or artists and designers. Central to these first two objectives is the continued development and refinement of an existing standard data format with rich metadata (and accessible metadata guides) according to common standards such as the Darwin Core. Third, we will establish a peer-reviewed archive (including tests by selected users) for data processing and analytical codes (that can be applied to the datasets hosted in the database, but also beyond), promoting efficiency and replicability of large-scale data analyses. Our final goal is a fully transparent FAIR research landscape of datasets, analytical tools and publications that are connected by digital identifiers and rich metadata.