Today's ready availability of data has not supplanted traditional hypothesis-driven methods of research, but it has created a more fluid interplay between the exploration of data and the formulation of hypotheses. Rather than progress in a linear fashion from observation to hypothesis, researchers might begin with a scientific challenge and gradually refine a hypothesis as they explore the data. In other words, researchers in many fields don't rely on data exclusively to prove a hypothesis. They also use it to help formulate one. And to do this, they need better tools to help them grasp the meaning of data. Associate Professor Gustavo Rohde's Imaging and Data Science Lab in the University of Virginia Department of Biomedical Engineering has responded to this shift by creating tools that help researchers more productively explore data. “Our lab is focused on creating computational methods that allow scientists to interact with and explore datasets in biology and medicine so that they can begin asking questions,†Rohde said. In March, Rohde received a grant from the National Institutes of Health to devise a new class of modeling tools that are better adapted to exploring biomedical data than the tools currently available.