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Virtual Grower 3

    Initially designed to help greenhouse growers determine heating costs and do simple simulations to figure out where heat savings could be achieved, it has slowly added features so that now, Virtual Grower can help not only identify those savings through different greenhouse designs, but predict crop growth, assist in scheduling, make real-time predictions of energy use, and see the impact of supplemental lighting on plant growth and development. In other words, the software can be a safety net and allow users to experiment with "what if" scenarios in a risk-free setting.

    CALMIM

      The California Landfill Methane Inventory Model is a 1-dimensional soil gas transport and oxidation model that calculates annual landfill methane emissions based on the cover soil characteristics and annual climatic data for a given global location.

      Data from: Quality controlled research weather data – USDA-ARS, Bushland, Texas

        The dataset contains 15-minute mean weather data from the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) for all days in 2016. The data are from sensors deployed at standard heights over grass that is irrigated and mowed during the growing season to reference evapotranspiration standards.

        Feedstock Readiness Level Evaluations Summary Table v4.1

          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.

          Feedstock Readiness Level Evaluations Summary Table v4.0

            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.

            Data from: Chromosome-level genome assembly and transcriptome of the green alga Chromochloris zofingiensis illuminates astaxanthin production

              For genome assembly of *C. zofingiensis* strain SAG 211–14, we used a hybrid approach blending short reads (Illumina), long reads (Pacific Biosciences of California), and whole-genome optical mapping (OpGen) (SI Appendix, SI Text and Datasets S1–S19, and refer to SI Appendix, Datasets Key). The combined power of these approaches yielded a high-quality haploid nuclear genome of *C. zofingiensis* of ∼58 Mbp distributed over 19 chromosomes (Fig. 2) in the tradition of model organism projects, as opposed to the fragmentary “gene-space” assemblies typical of modern projects using high-throughput methods and associated software. Approximately 99% of reads from the Illumina genomic libraries were accounted for, and nonplaceholder chromosomal sequence covers ∼94% of the optical map. Because no automated pipeline was found able to achieve the desired quality, methods are described in SI Appendix, SI Text.

              NWRC Chemical Effects Database

                The NWRC Chemical Effects Database is a searchable database that contains bioassay records and data for chemicals analyzed and evaluated for repellency, toxicity, reproductive inhibition, and immobilization. Studies were conducted from 1943 to 1987 by the U.S. Department of Agriculture's National Wildlife Research Center (NWRC), its predecessors, and by the U.S. Geological Survey's Patuxent Wildlife Research Center (PWRC) (formerly part of the U.S. Fish and Wildlife Service). The online database comprises published data only. The entire database, including unpublished data, is currently searchable by NWRC staff.

                Dairy Gas Emissions Model (DairyGEM)

                  The Dairy Gas Emissions Model (DairyGEM) uses process level simulation and process related emission factors to predict ammonia, hydrogen sulfide, VOC and greenhouse gas emissions along with the carbon, energy and water footprints of dairy production systems.