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Open source and reproducible and inexpensive infrastructure for data challenges and education

Written by Nature on January 2, 2024

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.

Topics: Press Coverage

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