Recent developments in our ability to capture, curate, and analyze data, the field of data-intensive science (DIS), have indeed made these interesting and challenging times for scientific practice as well as policy making in real time. We are confronted with immense datasets that challenge our ability to pool, transfer, analyze, or interpret scientific observations. We have more data available than ever before, yet more questions to be answered as well, and no clear path to answer them. We are excited by the potential for science-based solutions to humankind's problems, yet stymied by the limitations of our current cyberinfrastructure and existing public policies. Importantly, DIS signals a transformation of the hypothesis-driven tradition of science ("first hypothesize, then experiment") to one that is typified by "first experiment, then hypothesize" mode of discovery. Another hallmark of DIS is that it amasses data that are public goods (i.e., creates a "commons") that can further be creatively mined for various applications in different sectors. As such, this calls for a science policy vision that is long term. We herein reflect on how best to approach to policy making at this critical inflection point when DIS applications are being diversified in agriculture, ecology, marine biology, and environmental research internationally. This article outlines the key policy issues and gaps that emerged from the multidisciplinary discussions at the NSF-funded DIS workshop held at the Seattle Children's Research Institute in Seattle, on September 19-20, 2010.
© Mary Ann Liebert, Inc.