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For clinical biobanking, facilitating such integration involves huge amounts of work aimed at standardization and harmonization of data and tissue provision as well as efforts aimed at establishing quality control, certification of workflows, substantial and procedural benchmarks for data and tissue collection and management and evidence-based data models (Riegman et al. 2006; Mook 2011)).
Data standards are an essential prerequisite to any large scale information infrastructure because they provide benchmarks for data quality and define how information are formatted and categorized in order to be stored, managed, searched for and used across many different software applications (Bowker, 2008).
There is a need to establish robust benchmarks for data analysis for the comparison of different analytical approaches/software.
Benchmarks for data completeness and accuracy were set at 95.0%; kappa and intraclass coefficient (ICC) calculations were also utilised to supplement this analysis.
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57.1% (28/49) of variables assessed met the 95.0% benchmark for data accuracy.
Deformation was calculated using benchmarks BM34 (for data between April 2006 and April 2007) and BM25 (for all other data periods) as references (See Fig. 1 for locations of BM34 and BM25).
This model is an extension of the Poisson regression model, the benchmark for count data, which accounts for over-dispersion of data (Coxe et al., 2009).
We propose a micro-benchmark for XML data management to aid engineers in designing improved XML processing engines.
Currently, there are no standard benchmarks for normalized CGH data that take the three properties into account.
These datasets will serve as benchmarks for future research on data mining methods for CGH data.
JDL suggested the concept of HIV health care benchmarking, contributed with ideas for data analysis, interpretation of data, and writing the manuscript.
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