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Evaluating accuracy of DNA pool construction based on white blood cell counts

    Pooling individual samples prior to DNA extraction can mitigate the cost of DNA extraction and genotyping; however, these methods need to accurately generate equal representation of individuals within pools. This data set was generated to determine accuracy of pool construction based on white blood cell counts compared to two common DNA quantification methods. The dataset includes: 1) pooling allele frequencies (PAF) for all pools and individual animals computed from normalized intensities for red (X) and green (Y); PAF = X/(X+Y). 2) Genotypes or number of copies of B(green) allele (0,1,2). 3) Definitions for each sample.

    Data from: Temporal and agricultural factors influence E. coli survival in soil and transfer to cucumbers

      Data from the current study were collected to examine the survival of non-pathogenic Escherichia coli and transfer to cucumbers grown in same field in two separate years. Soil moisture, total nitrogen, nitrate, total carbon, soluble carbon, soluble solids, rainfall, soil temperature and air temperature, along with the number of days needed for E. coli to decline by 4 log CFU/gdw, were included in random forest models used to a) predict 4-log declines of E. coli inoculated to soils and b) transfer of E. coli to cucumbers from soils with different biological soil amendments.

      SGP97 Surface: NOAA/ATDD Little Washita, Oklahoma Long Term Flux Site

      NAL Geospatial Catalog
        The temporal coverage for this dataset is as follows: Begin datetime: 1997-05-31 00:00:00, End datetime: 1997-08-09 23:59:59. NOAA/ATDD (Tilden Meyers) started operation of a long term flux monitoring site near the Little Washita watershed in Oklahoma in 1996. Half-hourly observations of wind speed and direction, air temperature, relative humidity, pressure, incoming global radiation, incoming and outgoing visible radiation, net radiation, ground heat flux, precipitation, wetness, skin temperature, soil temperature (at 2, 4, 8, 16, 32 and 64 cm), average wind vector speed, kinematic shear stress, streamwise velocity variance, crosswind velocity variance, vertical velocity variance, sensible heat flux, latent energy flux, CO2 flux and soil moisture at 20 cm (started 5 June 1997).

        Sorption Isotherm Spreadsheet

          Spreadsheet from the paper entitled: On the Use of Linearized Langmuir Equations by C.H. Bolster and G.M. Hornberger, Soil Science Society of America Journal, 2007, 71(6): 1796-1806.

          Data from: Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

            The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline (http://probes.pw.usda.gov/bioinformatics_tools/MADPipeline/index.html).