Sentence examples for commonly accepted data from inspiring English sources

Flawed analysis either intentional or though misunderstanding of commonly accepted data analysis methods can lead to erroneous results and presumption of correlation of cause and effect, when, in fact, there is little or none.

In addition, scientific journals are challenged with formulating, implementing, and enforcing commonly accepted data deposition guidelines and addressing problems associated with the use of proprietary data.

Steps in metagenome analyses that can be automated should be automated to ensure quality, but this requires the establishment of commonly accepted data formats for metagenome sequences and their associated contextual (meta data, as well as defined interfaces for data exchange and integration a task that is tackled by the Genomic Standards Consortium (GSC, http://gensc.org) [ 132].

However, it is commonly accepted that data obtained in aqueous solution allow to predict the surface tension effects, and to model the behaviour of organic solvents.

Although the non-haematological origin of MSCs is generally accepted, data suggest that the distinction between haematopoietic and non-haematopoietic cells may not be as sharp as commonly believed.

The highest possible degree of interoperability and re-usability of the contents of databases requires the development of commonly accepted standards for data and metadata – a process that already has been initiated in various biomedical communities (e.g. [1]).

Using commonly accepted methods of data reduction such as factor analysis and reliability analysis, the survey items are grouped to represent quality indicators, resulting in a quality rating for each indicator (also known as composites) [ 8, 9].

This, together with the implementation of external quality testing projects and the generation of a commonly accepted framework to report data from immunomonitoring assays [ 15], is a major international challenge for achieving comparability of data across institutions.

Hadoop is a commonly accepted framework for big data analytics.

These reliability statistics exceed commonly accepted criteria for group data and therefore indicate that the CASIS has good reliability [ 21, 26].

Within other related "-Omics" communities such as proteomics, transcriptomics and genomics it is a commonly accepted practice to share data by submitting their results to a public repository during or prior to submission of their manuscript, resulting in shared high-quality, structured data [ 1].