Abstract
The "omics" revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level. The volume of "big" data generated by the different omics studies such as genomics, transcriptomics, proteomics, and metabolomics has led to the concurrent development of computational tools to enable in silico analysis and aid data deconvolution. Considering the intensive resources and high costs required to generate and analyze big data, there has been centralized, collaborative efforts to make the data and analysis tools freely available as "Open Source," to benefit the wider research community. Pancreatology research studies have contributed to this "big data rush" and have additionally benefitted from utilizing the open source data as evidenced by the increasing number of new research findings and publications that stem from such data. In this review, we briefly introduce the evolution of open source omics data, data types, the "FAIR" guiding principles for data management and reuse, and centralized platforms that enable free and fair data accessibility, availability, and provide tools for omics data analysis. We illustrate, through the case study of our own experience in mining pancreatitis omics data, the power of repurposing open source data to answer translationally relevant questions in pancreas research.