Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, DBMI researchers developed a workflow that allows for reproducible model training, testing, and evaluation.
