U.S. flag

An official website of the United States government

Data from: Use of long-read sequencing simulators to assess real-world applications for food safety

    The goal of this project was to use NanoSim-H software to simulate Oxford Nanopore sequencing reads to assess the feasibility of sequencing-based foodborne pathogen detection and guide experimental design. Sequencing reads were simulated for STEC, *L. monocytogenes*, and a 1:1 combination of STEC and *Bos taurus* genomes using NanoSim-H. This dataset includes all of the simulated reads generated by the project in fasta format.

    Minimum Inhibitory Concentration (MIC) data for third generation cephalosporin resistant E. coli and extended spectrum beta-lactamase producing Enterobacteriaceae from feedlot cattle

      The data presents the antimicrobial susceptibility testing results in three separate files: 1) third generation cephalosporin resistant E. coli isolates obtained on cefotaxime supplemented media; 2) extended spectrum beta-lactamase (ESBL) producing E. coli, and 3) ESBL-producing Klebsiella, Enterobacter and Citrobacter species obtained on chromogenic media. The data was generated as part of a research project that evaluated the impact of tylosin supplementation of feedlot cattle on the dynamics of antimicrobial resistant fecal bacteria. The study was a longitudinal design with periodic sampling of fecal samples from individual animals over the entire feeding period.

      Elderberry syrup buffer modeling data

        Data for the article: Modeling the Formulation pH of Elderberry Syrup with multiple weak acids, https://doi.org/10.1111/1750-3841.16664. To develop methods to assess the influence of the ingredients of an acidified elderberry syrup on product pH., a total of 16 model syrup formulations containing elderberry juice with mixed acids (malic, acetic, and ascorbic) and having pH values between 3 and 4 were prepared. The pH values of the formulations were compared to predicted values from combined buffer models of the individual ingredients. Regression analysis indicated an excellent fit of the observed and predicted pH data, with a root mean square error of 0.076 pH units. The results indicated that buffer models may be useful for in silico estimates of how the ingredients in acid and acidified foods may influence pH, thus aiding in product development and safety assessments.

        Pesticide Data Program (PDP)

          The Pesticide Data Program (PDP) is a national pesticide residue database program. Through cooperation with State agriculture departments and other Federal agencies, PDP manages the collection, analysis, data entry, and reporting of pesticide residues on agricultural commodities in the U.S. food supply, with an emphasis on those commodities highly consumed by infants and children. This dataset provides information on where each tested sample was collected, where the product originated from, what type of product it was, and what pesticide residue was found on the product, for calendar years 1992 through 2020.

          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.

            USDA Branded Food Products Database

              The USDA Branded Food Products Database is the result of a Public-Private Partnership, whose goal is to enhance public health and the sharing of open data by complementing USDA Food Composition Databases with nutrient composition of branded foods and private label data provided by the food industry. [Note: Integrated as part of FoodData Central, April 2019.]