Core C: Computation and Software Development Core The overall goal of Core C is to provide researchers involved with the Program Project with support and guidance for all their computational needs while developing their code, as well as assistance with implementation of algorithms in the form of scientific software. As such, Core C will play a central role in the success and impact of each of the research projects. We have organized the Core activities into two main aims: High-Performance Computing [HPC] and Scientific Code Development. In doing so, we are conscious of two key concepts: Reliability, and Scalability. Our first Specific Aim focuses on high-performance computing. The Core will be involved in activities ranging from developing and allocating resources for computation, to actively reviewing or developing new methods for dealing with data that may be both ‘big’ and complex. Specifically, we will focus on the use of parallel computing, cloud computing technologies, and out-of-memory data analysis techniques. Our second Specific Aim focuses on scientific code development. Software tools are a corner- stone component when it comes to the diffusion and wide-spread use of novel statistical methods. The Core will assist members in developing the code for such software, with a special emphasis on the efficient implementation of algorithms, based on two fundamental principles, reliability and scalability. To this end, we will develop standards and guidelines on scientific software development--including themes such as documentation, version control, continuous integration and unit tests--to be followed by the project members. Furthermore, Core C will both advise and actively participate in the implementation process of the methods developed by each project. Ultimately, the Core will be involved throughout the process all the way from initial algorithmic design and implementation of the methods, up to final software design, packaging and public release. Additionally, the Core will play a number of other key roles supporting the other Program Project components. Examples of these include resource allocation, simulation of test data, and development of computational-methods tutorials that can be distributed in partnership with the educational efforts of Core D.