Project Summary This Administrative Supplement to the Idaho INBRE Program will develop new cloud-based learning modules. The project will expand the NIGMS Sandbox repository with modules that can be incorporated into curricula, workshops, and training. This funding will increase the capacity of Idaho to participate in cutting-edge biomedical research. The activity is within the scope of the parent INBRE award #P20 GM103408. It fits within the broad and inclusive Idaho INBRE activities to improve the state’s capacity to do biomedical research and provide STEM training and research skills to capable students. Specifically, we will construct a set of self- paced, self-learning modules to teach foundational bioinformatics programming in Python, and version control with git and GitHub. The proposed modules will be appropriate for integration into undergraduate/graduate curricula and for self-learning by individuals who are new to biology research. Most of the current GitHub NIGMS Sandbox modules use the Python programming language, extensively. However, no module teaches Python. This is a barrier to participation since faculty and students alike often lack the coding skills to perform data analysis independently by adapting scripts to novel circumstances. There is thus a critical need for a Sandbox module which will teach foundational computing skills of Python programming and about GitHub and git. Our overall objective is to develop a module that will enable users to use Python and git to store, analyze, and draw inferences from biological data with cloud computing. Three Specific Aims will be addressed: (1) Adapt our current Foundations of Python for Data Science course to a cloud-based module for the NIGMS Sandbox (2) Develop exercises that incorporate bioinformatic data sets and questions for each lesson and a final project for each unit. (3) Test the modules with undergraduate and graduate students in biology. This new Sandbox module is expected to have a positive impact on (i) FAIR data sharing via GitHub repositories, (ii) the innovative use of bioinformatic analysis to solve biological problems because researchers will have the Python skills to adapt and use tools and, (iii) the pipeline of students who have the coding skills to use cloud computing to analyze, process, and share biological data.

Project Narrative Large datasets in biological studies are often stored in Cloud repositories and analyzed by fast cloud computing built on Python programs. Many biology students and biomedical researchers may be unprepared for the coding demands of using these tools. We propose to build a module to train scientists to code in Python so that they can use advanced tools to interpret complex biological data sets.