No country for old data: Increasing FAIR-ness of research outcomes through standardization and community-driven development

The current status of scientific research and academia is defined by a multitude of high-dimensional, interacting factors. At the core, especially aspects like decentralization, digitalization, “big data” and “the reproducibility crisis” shaped recent discussions and continue to do so. Common to all of these aspects and one of the driving forces of cumbersome scientific progress is that the majority of research outcomes is not FAIR (Findable, Accessible, Interoperable, Reusable). While a myriad of potential solutions and approaches to tackle these problems were proposed, the combination of comprehensive standardization (e.g., The Brain Imaging Data Structure) and community-driven development of resources (e.g., the open science/brainhack community, The Turing Way) manifested itself as a well-functioningand sustainable way forward. Throughout the life cycle of a scientific project, the respective implementations, chances and limitations of this approach will be discussed and its capacities to increase FAIR-ness outlined based on examples. Furthermore, it will be addressed how this approach can drastically enhance scientific progress through creating a more open and inclusive form of academia.