DBgen provides a framework that makes it easy to implement SMART ETL pipelines. We believe this will have the largest impact in the field of scientific research. Specifically, we believe that DBgen will result in more scientific data being stored in accordance with the FAIR principles of data management. Ultimately, this will accelerate innovation in computational methods applied to experimental scientific data. As a point of analogy, Ima-geNet was published in 2009 and played a significant role in the development of new convolutional neural network architectures shortly thereafter, as evidenced by the publication of AlexNet in 2012 , and Resnet in 2016 .