Modern microscopy generates imaging data across a vast range of spatio-temporal scales and at various resolutions, broadening the extent of observable morphological features in biological systems. Acknowledging that morphology information is present in most types of microscopy data, the Uhlmann group at EMBL-EBI develop general-purpose, modality-agnostic methods for bioimage quantification. In this talk, I will first present some of our efforts in automating the extraction of data-driven morphology descriptors from a range of microscopy images, and towards proposing novel data analysis techniques to mine collections of such measurements. I will then give an overview of the BioVisionCenter, a new initiative for FAIR bioimage analysis at the University of Zurich that I am leading since the beginning of the year. There, we aim at bridging the gap between novel computer vision method development and their broader application at scale on microscopy data, to ultimately allow bioimage analysis methods to become widely usable tools.