Data competitions often rely on the physical distribution of data to challenge participants, a significant limitation given that much data is proprietary, sensitive, and often non-shareable. To address this, the DREAM Challenges has advanced a challenge framework called modelto-data (MTD), requiring participants to submit re-runnable algorithms instead of model predictions. The DREAM organization has successfully completed multiple MTD-based challenges, and is expanding this approach to unlock highly sensitive and non-distributable human data for use in biomedical data challenges.