Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging

High-content screening (HCS) bioimaging approaches are powerful techniques consisting of the automated imaging and analysis of large numbers of biological samples, to extract quantitative and qualitative information from the images. HCS still presents several bottlenecks restraining these approaches from exerting their full potential for scientific discoveries. As major example, a huge amount of metadata is generated in each experiment, capturing critical information about the images. The efficient and accurate treatment of image metadata is of great importance, as it provides insights that are essential for effective image management, search, organisation, interpretation, and sharing. It is vital to find ways to properly deal with the huge amount of complex and unstructured data for implementing Findable, Accessible, Interoperable and Reusable (FAIR) concepts in bioimaging.