Gen-FS coordinated proficiency test data for genomic foodborne pathogen surveillance, 2017 and 2018 exercises

The US PulseNet and GenomeTrakr laboratory networks work together within the Genomics for Food Safety (Gen-FS) consortium to collect and analyze genomic data for foodborne pathogen surveillance (species include Salmonella enterica, Listeria monocytogenes, Escherichia coli (STECs), and Campylobactor). In 2017 these two laboratory networks started harmonizing their respective proficiency test exercises, agreeing on distributing a single strain-set and following the same standard operating procedure (SOP) for genomic data collection, running a jointly coordinated annual proficiency test exercise. In this data release we are publishing the reference genomes and raw data submissions for the 2017 and 2018 proficiency test exercises.

Proficiency tests with uncertainty information: analysis using the maximum likelihood method

<p><span style="font-size: small;"><span style="font-family: Calibri;">In this study, we report the application of the maximum likelihood method to the analysis of proficiency test data when uncertainty information is given and a reference laboratory does not exist. There are two causes that could impair the quality of analysis using the maximum likelihood method: the existence of an unknown random effect, and outliers. The conditions under which performance evaluations can be appropriately conducted are discussed in this study. To avoid serious impacts from these two causes, the maximum permissible standard uncertainty of an unknown random effect and the minimum permissible standard uncertainty of the values reported by a participating laboratory are quantified. Through simulations, the maximum and the minimum permissible standard uncertainties are found to be 0.3 and 0.5 times the intermediate magnitude of the standard uncertainty that the participants are expected to report. We believe that our proposed procedure based on these criteria is sufficiently simple to be employed in actual proficiency tests. </span></span></p>

Geological materials testing and uncertainty calculations: a simple GUM-based algorithm

We have applied a simple GUM-based procedure to estimate the uncertainties of physical and mechanical properties in geological materials. First, we define the quantity to measure and decide whether we want to work with units or relative quantities. Subsequently, we calculate the repeatability standard deviation (sr) and the standard uncertainty. If we have proficiency test data or use certified reference materials, we use them to estimate the laboratory bias, the reproducibility standard deviation (sR) and the reproducibility standard uncertainty. We also make sure that we know or have estimated the standard uncertainty of the instruments that we use in the measurements. The latter is typically taken from the instrument calibration or precision statement. We estimate the standard uncertainty of the reference materials and the standard uncertainty of the laboratory bias. The final two steps include the calculation of (1) the laboratory standard uncertainty uncorrected for bias and corrected for bias, and (2) the laboratory expanded uncertainty at the 95% confidence limit.