Building a Next Generation Image Repository: Molecular Annotation and Cloud-based Data Processing and Analysis

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Access to primary research data is vital for the advancement of the scientific enterprise. It facilitates the validation of existing observations and provides the raw materials to build on those observations. In the life sciences, there are numerous examples where members of a research community determined that a particular type of data would be useful and necessary to share. These include gene sequences, protein structural data, and gene and protein expression profiles. In these cases the community united to standardize the structure of the data and its associated metadata, and to create centralized repositories to facilitate deposition, promote discoverability, and ensure the longevity of the data. Imaging in the life sciences has undergone a revolution in recent years and is now used as a quantitative assay technology throughout the life and biomedical sciences. Imaging is used to understand the behavior of organisms, the formation of embryos, the structure and dynamics of cells, and the function and interactions of molecules that are the building blocks of life. Imaging datasets are complex, heterogeneous, and often extremely large, so they are rarely shared or published. Based on the recent development of several image data management technologies and the rapidly decreasing cost of large data storage facilities, we propose to create a resource to host, serve, and make available original scientific image data that underpins life sciences research. Our proposal is based on open source technologies with proven utility and performance that already run on-line resources serving several terabytes (TBs) of image data. We propose to place this resource at EMBL-EBI, which is the established home of molecular and structural life sciences data and interface the resource with ELIXIR, Europe's research infrastructure for life science informatics. In particular we will build links with established molecular and structural resources and work towards a seamless integration of these data, so that any scientist can easily browse, query and compute on genomic, structural and phenotypic data across several scales.