Data management approach to multidisciplinary agricultural research and syntheses

Agricultural scientists are increasingly asked to address challenges related to natural resource stewardship, agricultural productivity, and environmental protection while simultaneously being mindful of the impact and risks associated with climate change (ASA CSSA SSSA 2011; Hatfield et al. 2011; OSTP CAST 2012). Measuring and predicting the effects of climate on agricultural systems adds a layer of complexity that is challenging using traditional data management methods; these methods also limit the potential for full data discovery and innovation (Overpeck et al. 2011; Wolkovich et al. 2012). To properly address challenges, access to multidisciplinary data spanning environments, timescales, treatments, and management is necessary (White and van Evert 2008; Reichman et al. 2011; Eigenbrode et al. 2014). Disciplinary scientists, data scientists, and data managers need to increasingly work in a collaborative manner in this data-rich era. While scientists generally desire to share data, time constraints, limited funding, a lackluster reward system, and reuse concerns are cited as barriers (Michener et al. 2011; Tenopir et al. 2011; Marx 2012; Wolkovich et al. 2012). A concerted and well-executed approach is necessary to overcome these barriers and move toward transformative science. The Climate and Corn-based Cropping Systems Coordinated Agricultural Project (CSCAP), referred to as “Sustainable Corn,”…