Using of Validated Software for Uncertainty Analyses Tools in Accredited Laboratories

2008 ◽  
Vol 381-382 ◽  
pp. 599-602 ◽  
Author(s):  
O. Velychko

According to international standards the accredited calibration and testing laboratories are required to use reliable measuring instruments and to estimate an uncertainty of measurements. The variety of software tools and the different approaches taken will almost certainly ensure that for each laboratory there is a software package which will meet the needs. In this paper, several a software packages are made according to their validation for comparison. Briefly general principles of validating uncertainty analysis software packages are described. Briefly used validation methods are described.

2021 ◽  
Author(s):  
Oleh Velychko ◽  
Tetyana Gordiyenko

National accreditation agencies in different countries have set quite strict requirements for accreditation of testing and calibration laboratories. Interlaboratory comparisons (ILCs) are a form of experimental verification of laboratory activities to determine technical competence in a particular activity. Successful results of conducting ILCs for the laboratory are a confirmation of competence in carrying out certain types of measurements by a specific specialist on specific equipment. To obtain reliable results of ILC accredited laboratories, it is necessary to improve the methods of processing these results. These methods are based on various data processing algorithms. Therefore, it is necessary to choose the most optimal method of processing the obtained data, which would allow to obtain reliable results. In addition, it is necessary to take into account the peculiarities of the calibration laboratories (CLs) when evaluating the results of ILС. Such features are related to the need to provide calibration of measuring instruments for testing laboratories. The evaluation results for ILCs for CLs are presented. The results for all participants of ILCs were evaluated using the En and z indexes. The obtained results showed that for the such ILCs it is also necessary to evaluate the data using the z index also.


2018 ◽  
Author(s):  
Alexander Bowring ◽  
Camille Maumet ◽  
Thomas E. Nichols

AbstractA wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this ‘methodological plurality’ comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.In this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.


Author(s):  
Peter M. Rice ◽  
Keith EHiston

Software packages are available for all common laboratory computer systems. The packages for personal computers (PC or Macintosh) are able assemble and correct the sequence, those for the larger systems (VAX or Unix) are generally able to analyze the sequence in greater detail. Most laboratories will be able to use sequence assembly programs in their favorite sequence analysis software package. In general, the stages of sequence assembly are gel entry, overlap detection, editing, and reporting. The available programs differ in the ways they handle each of these tasks. No single package is ideal, though all should be adequate for a smaller project such as a single cDNA. Particular attention should be given to the quality and features of the editor, as this is where most time will be spent, and to the possibilities of extending the software to cope with problems that may arise. Good status reports and a choice of methods for overlap detection can save considerable time in resolving ambiguities and correcting errors later. Figure 1 lists some of the commonly used sequence assembly programs. The prices vary widely depending on the features of the package and the options for academic or commercial licenses. Originally, each package used its own “special” codes to represent ambiguous bases and gaps in sequences. Mostpackages now use the standard IUB-IUPAC codes (Figure 2) for the nucleotides, though the program documentation should be checked before starting the project. The task of sequence reading depends on the sequencing protocol used. In many laboratories the sequence is generated on an autoradiograph (Figure 3) from which the sequence is read. Although automated gel readers are on the market, most sequence data is read manually with the aid of a digitizer. Most sequence assembly programs accept DNA sequence read by a sonic digitizer. An example of a device which is supported by most of the available programs is the GrafBar GP-7 [Science Accessories Corporation, Southport, CT, US A and P.M.S. (Instruments) Ltd., Waldeck House, Reform Road, Maidenhead, Berks, SL6 8BX, UK]. Sonic digitizers have a stylus to point to locations on an autoradiograph, which is illuminated from below by a light box.


2020 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Ann-Marie Mallon ◽  
Damian Smedley ◽  
Terrence F Meehan ◽  
...  

AbstractReproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.


EDIS ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 7
Author(s):  
Jeffry M. Flenniken ◽  
Steven Stuglik ◽  
Basil V. Iannone

Geographic information system (GIS) software packages can be prohibitively expensive, causing many to shy away from mapping and spatial analysis. This 7-page fact sheet written by Jeffry M. Flenniken, Steven Stuglik, and Basil V. Iannone III and published by the UF/IFAS School of Forest Resources and Conservation introduces the reader to a free GIS software package called Quantum GIS (QGIS), walking the reader through simple GIS processes that can be used to visualize spatial patterns of importance to a variety of fields, including natural resources, agriculture, and urban planning. Learn how to create a land-cover map for a county of interest and create heatmaps that illustrate the density of a given attribute (Florida Springs for this example). This publication will benefit those interested in incorporating GIS into their work but who are unable to afford expensive proprietary GIS software packages, as well as anyone interested in learning a new GIS software package. https://edis.ifas.ufl.edu/fr428


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