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Title: Driving Innovation through Data in Agriculture (DIDAg): Best Practices and Case Study

Author
item MOORE, ELI - Rowan University
item KRIESBERG, ADAM - Simmons University
item Schroeder, Steven - Steve
item HAUGEN, INGA - Virginia Polytechnic Institution & State University
item BARFORD, CAROL - University Of Wisconsin
item JOHNS, ERICA - Cornell University
item Arthur, Dan
item SHEFFIELD, MEGAN - Clemson University
item RITCHIE, STEPHANIE - University Of Maryland
item JACKSON, CAROLYN - Texas A&M University
item Parr, Cynthia - Cyndy

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/29/2021
Publication Date: N/A
Citation: N/A

Interpretive Summary: Agricultural researchers have little guidance on data management best practices . We report on the results of two workshops held in 2018 and 2019 on how to support innovation in agricultural research via good data management. Recommendations include: Metadata review can allow for peer review to ensure that the value of agricultural data is maintained. Minimal data sets should be defined by a research community as the core data and associated information that is needed to ensure that research data can be re-used. Data repositories should be used and should promote best practices in many ways. Engaging citizens in agricultural research can enhance data availability and adoption of research results. Funders, journals, institutions, librarians, and researchers can all support good data management. A case study in dairy agroecosystems illustrates how data management best practices can be used in research to reduce greenhouse gas emissions.

Technical Abstract: Agricultural data are crucial to many aspects of production, commerce, and research involved in feeding the global community. However, standard best practices for agricultural research data management and publication do not exist given the wide range of disciplines associated with agriculture. Here we propose a set of best practices in the areas of peer review, minimal dataset development, data repositories, citizen science initiatives, and support for good data management. We illustrate some of these best practices with a case study in dairy agroecosystems research. While many common, and increasingly disparate data management and publication practices are entrenched in agricultural disciplines, opportunities are readily available for promoting and adopting best practices that better enable and enhance data-intensive agricultural research and production.