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Abstract The US agriculture and food systems research and education system remains the envy of the world, and the US Department of Agriculture and the Land-Grant University system lead the public and private partnerships that have improved agricultural productivity and human health phenomenally for over 160 years. The continuation of these improvements relies on equitable access to trustworthy data—particularly in genetics and phenomics—and the ability to leverage such data to address future scientific challenges. In this article, we discuss the growing need in agriculture for phenomic databases that follow findable, accessible, interoperable, and reproducible data (FAIR) guidelines, as well as the need for public policy supporting a sustainable funding model for these databases. 

Abstract BackgroundVariation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. ResultsWe demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. ConclusionsRVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring. 

Abstract Producers desire cultivars that consistently perform with high yields and end‐use qualities. Unlike easily recognized average yield improvements, yield stability over time is less examined, especially when considering the role of breeding relative to other factors like management and changing climatic conditions. Our study system was a 70‐year historical dataset from which we estimated the year‐over‐year stability of winter wheat (Triticum aestivumL.) cultivars released by Montana's Agricultural Experimental Station. We examined yield stability within six locations representing diverse growing conditions across Montana and found no evidence that breeding has improved stability, that stability may be decreasing over time at one location, and that the year‐over‐year stability of a cultivar is sensitive to location. We examined the role of climatic conditions, including temperature, and rainfall to understand if increased climatic variability was masking improved patterns of stability. However, the lack of impact of breeding remained. These findings suggest that Montana's winter wheat may benefit from selective breeding for increased stability within locations.