Datasets of all types are being generated at a fast pace. A central challenge for life science researchers is sharing, integrating, and analyzing disparate data types to discover fundamental biological principles. This project seeks to address that challenge by leveraging the tools and cyberinfrastructure developed through the NSF BIO-funded CyVerse project (CyVerse.org; DBI-12565383) to develop and test novel computational solutions that will allow the vast imaging data housed in the Cancer Imaging Archive to be shared and analyzed with other relevant data types. The results of this work should provide new, much-needed tools for the cancer research community. In addition, the outcomes should serve as the basis for developing new solutions for integrating data types for the broader biomedical and basic research communities. Data-driven discovery, especially in the field of cancer research, is hampered by poor interoperability across data storage platforms and limited access to tools for computational integration of large, heterogeneous datasets. To address these limitations, two pilot projects will be conducted. The first will integrate TCIA image data with CyVerse's image management and analysis capabilities, developing a proof-of-concept model for how researchers can rapid manage and analyze high-quality cancer image data. The second pilot will enable cancer researchers at University of Arizona Cancer Center and elsewhere to aggregate their public and private cancer image data using CyVerse's capabilities and to integrate image and genomic data via a custom user interface. Together, the results of these pilots will provide researchers with the computational systems and technology needed to more rapidly manage, analyze, and disseminate their work.