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Maize-GAMER: Maize B73 RefGen_v3 5b+

    This dataset from maize-GAMER is a new high-coverage and reproducible functional annotation of maize (*Zea mays*) protein coding genes based on Gene Ontology (GO) term assignments that covers all genes in the B73 RefGen_v3 5b+ set. Data are compressed gzip (.gz) files.

    USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013

      The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (*Zea mays* L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information.

      Maize Genetics Cooperation Stock Center Catalog of Stocks

        The Maize Genetics Cooperation Stock Center is operated by USDA/ARS, located at the University of Illinois, Urbana/Champaign, and integrated with the National Plant Germplasm System (NPGS). The center serves the maize research community by collecting, maintaining and distributing seeds of maize genetic stocks, and providing information about maize stocks and the mutations they carry through the Maize Genetics and Genomics Database (MaizeGDB).

        Maize-GAMER: GO Annotations, Methods, Evaluation and Review

          maize-GAMER is a collaborative project to improve the status of gene functional annotation in maize (*Zea mays*). The project has three main areas of focus, namely * Design a pipeline for the functional annotation of maize genes. * Use manually curated test data to evaluate the annotations and generate a best subset of annotations for use * Design a user friendly review system for the community to provide feedback and endorsements of the annotations

          Data from: Datasets for transcriptomic analyses of maize leaves in response to Asian corn borer feeding and/or jasmonic acid

            Corn's (*Zea mays*) response to initial insect damage involves a variety of changes to the levels of defensive enzymes, toxins, and communicative volatiles. Such a dramatic change secondary metabolism necessitates the regulation of gene expression at the transcript level. This *Data In Brief* paper summarizes the datasets of the transcriptome of corn plants in response to corn stalk borers (*Ostrinia furnacalis*) and/or methyl jasmonate (MeJA). Altogether, 39,636 genes were found to be differentially expressed.

            Low-Disturbance Manure Incorporation

              The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (*Zea mays*) / rye cover-crop (*Secale cereale*) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP.

              Feedstock Readiness Level Evaluations Summary Table v3.0

                The table in this dataset collates the results of the FSRL evaluations listed under the Farm2Fly Ag Data Commons datasets to enable users to quickly identify, review, and compare available evaluations. Feedstock readiness level evaluations are performed for a specific feedstock-conversion process combination and for a particular region. FSRL evaluations complement evaluations of Fuel Readiness Level (FRL) and environmental progress.

                Manure application methods for alfalfa-grass

                  The MAMA experiment (Manure Application Methods for Alfalfa-Grass), from the USDA-ARS research station in Marshfield, WI was designed to evaluate nutrient and pathogen losses with conventional and improved liquid dairy manure management practices for alfalfa-grass production. Observations from MAMA have also been used for parameterization and validation of computer simulation models of greenhouse gas (GHG) emissions from dairy farms.

                  Feedstock Readiness Level Evaluations Summary Table v2.0

                    The table in this dataset collates the results of the FSRL evaluations listed under the Farm2Fly Ag Data Commons datasets to enable users to quickly identify, review, and compare available evaluations. Feedstock readiness level evaluations are performed for a specific feedstock-conversion process combination and for a particular region. FSRL evaluations complement evaluations of Fuel Readiness Level (FRL) and environmental progress.