 Data-driven science is revolutionizing material science by utilizing data as a new resource to discover and improve materials or phenomena through machine learning and high throughput methods, but faces challenges such as data veracity, integration, longevity, standardization, and the gap between industrial and academic interests. This article was authored by Lori Himinan, Amber Schertz, Adam Stuart Foster and others.