Project Summary We propose constructing a module focused on training experimental scientists to interface with high-dimensional biomedical data generated using a variety of genomics platforms. In our combined experience training scientists to analyze high-dimensional biomedical data we have identified three major barriers to experimental biologists accessing, interfacing, and analyzing biomedical data. First and foremost, is an understanding of what the ‘cloud’ is and how to access various cloud computing systems. Secondly how to customize a cloud environment with tools appropriate for an analysis of interest, and finally error mitigation when using new software to access, manipulate, and analyze digitized biomedical data. Many freely available resources that introduce the cloud environment are not targeted for experimental biologists, and commonly lack the practice space and time necessary for some students to grasp foundational concepts before moving onto the next topic. Our proposed module uses biomedical data as a training set throughout the module and focuses on overcoming the three common barriers to learning bioinformatics. Each lesson will build on the previous lesson to provide a complex and well-rounded understanding of how cloud environments can be leveraged to interact with digitized biomedical data. Each lesson will begin with a video and example code, which will then be practiced in the virtual ‘sandbox’ environment, and followed by a self-test activity enabling students to determine if they are ready for the material in the next lesson. This format enables self-paced learning of foundational topics that specifically address canonical barriers to working with digitized biomedical data.