There are data from over 1.6 million open digital samples in the NCBI's Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo). GEO houses high quality experiments that the NIH has funded to measure the functional genomics of diseased and healthy individual samples. The majority of these samples interrogate cancer phenotypes and can be used to better characterize the genomics of that disease. However, GEO digital samples lack any structured biological annotations (bioannotations) because they are variably described across different experiments by free text attributes. This proposal is about using the Search Tag Analyze Resource (STAR) as a genomics discovery platform to crowdsource the precise bioannotation of this open Big Data. We will demonstrate the utility of a well-structured GEO to better characterize cancer functional genomics and to estimate a robust molecular nosology across the disease. The robust gene signatures we define is a first step towards a more comprehensive genomic understanding of the spectrum of the disease and making novel drug and biomarker discoveries. Therefore, successful funding and completion of this work has the potential to improve translational discoveries that greatly reduce the burden of disease on patients and thus improve overall health and wellbeing of society.