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Nearest Neighbor Soil Water Retention Estimator

    The k-nearest neighbor (k-NN) technique is a non-parametric technique that can be used to make predictions of discrete (class-type) as well as continuous variables. The k-NN technique and many of its derivatives belong to the group of .lazy learning algorithms.. It is lazy, as it passively stores the development data set until the time of application; all calculations are performed only when estimations need to be generated.

    Data from: Multiple immune pathways control susceptibility of Arabidopsis thaliana to the parasitic weed Phelipanche aegyptiaca

      Four files are included in this dataset. 1. An R script for generating odds ratio graphs that depict both the 95% and 99% confidence interval across all tested mutants in the referenced paper. 2. An example csv file for use with the R script. 3. A SAS script for running the Proc Glimmix procedure for generating odds ratios of attachments for all tested mutants in the referenced paper. 4. An example JMP file for use with the SAS script.

      Annotations of Unigenes Assembled from Schizaphis graminum and Sipha flava

        Transcriptomes were assembled de novo from pools of adult aphids that were feeding on sorghum and switchgrass. Reads from all replicates were pooled, normalized in silico to 25X coverage, and assembled using Trinity. Only the most abundant isoform for each unigene was retained for annotation and unigenes with transcripts per million mapped reads (TPM) less than 0.5 were removed from the dataset. The remaining unigenes were annotated using Trinotate with BLASTP comparisons against the Swiss-Prot/UniProt database. In addition, Pfam-A assignments were computed using hmmer, signal peptide predictions were performed using SignalP, and transmembrane domain predictions were performed using tmHMM. Gene ontology (GO assignments) were retrieved from Trinotate using the highest scoring BLASTp matches as queries.

        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.

            ELIGULUM-A regulates lateral branch and leaf development. Original figure files

              TIFF and JPEG files for the photographs used in constructing figures and supplemental figures in the manuscript, "ELIGULUM-A regulates lateral branch and leaf development," submitted to Plant Physiology. The images document a mutation that alters most of the structures of the plant and how the ELIGULUM-A gene interacts with different developmental pathways. The Figure Legend files describe the images individually.

              Genomes To Fields 2016

                Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/GenomesToFields_G2F_2016_Data_Mar_2018).