Critical Infrastructure to Promote Data Synthesis into Evidence-Based Nutrient Management.

As computing power has increased exponentially in recent years, “Big Data” has become the darling of science. However, most scientific datasets, including soil fertility and nutrient management datasets, are produced by small-scale individual studies; thus the science on a given topic may be represented by numerous, albeit relatively modest-sized, individual datasets dispersed across a broad array of public and private research programs. Cultural barriers including promotion, tenure and the general reward system for scientific achievement have been identified as persistent barriers to data sharing and greatly limit access to these small-scale datasets even as many Federal and State agencies have endorsed “open access” policies. Cultural norms will likely change in response to “open access” and funding agency requirements, but an even more challenging barrier is anticipated to largely prevent effective data sharing within and across disciplines and data’s reuse by other researchers: the non-standardized and heterogeneous way in which data are collected, annotated and stored and the general dearth of infrastructure to support both data sharing and its routine synthesis into evidence-based practice and policy. Failure to progress towards effective data sharing reflects the complexity of the issue from lack of education in core data competencies to a laissez faire attitude toward data structures that eschews adoption of rigid, overly detailed data collection templates that may exceed the needs of the immediate experiment. In this presentation we will use the objective of evidence-based nutrient management as a case study for suggestions to guide the development of data sharing best practices, to identify effective work-flows for extending data lifecycles, and to identify candidate business models for supporting data curation and preservation. The 4R Fund Research Repository will be discussed with respect to infrastructure sustainability issues and to emerging opportunities including the critically important task of informing recommendations with “dark” data.