Abstract
AbstractAgricultural data are crucial to many aspects of production, commerce, and research involved in feeding the global community. However, in most agricultural research disciplines standard best practices for data management and publication do not exist. 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 best 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.
Highlights
With the rise of smart farming technologies in agriculture leading to greater data creation and utilization by producers and researchers, many questions have arisen and still remain regarding data management throughout the agricultural sector (Wolfert et al, 2017)
Standard best practices for agricultural research data management and publication do not exist given the wide range of disciplines associated with agriculture
A wide range of best practices identified by Driving Innovation through Data in Agriculture (DIDAg) participants could replace data management common practices and improve data-intensive research in agriculture
Summary
With the rise of smart farming technologies in agriculture leading to greater data creation and utilization by producers and researchers, many questions have arisen and still remain regarding data management throughout the agricultural sector (Wolfert et al, 2017). Many repositories do not themselves offer peer review of research data but they do provide curation to ensure that metadata, methodology, and data processing are well described and consistent with FAIR principles. Following these principles can aid peer review by journals or data consumers, and generally make it easier for the others to use the information. Article peer reviewers could ensure that researchers deposit all their raw data into a repository with no filters or processing to allow a wider range of future analyses This approach has not yet achieved community acceptance in part because the data collector may not see the value of preparing the metadata. Accompanying data, such as associated microbial community data, can serve as an important type of metadata (i.e., one researcher’s data is another researcher’s metadata and vice versa)
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