Sentence examples for and relevance of data from inspiring English sources

This guidance should provide a transparent and science-based method to assess the reliability and relevance of data on NP (eco) toxicity data.

Quality of information (QoI) in sensor networks measures information attributes such as precision, timeliness, completeness, and relevance of data ultimately delivered to users.

This process inherently includes trade-off, as setting the boundaries impacts not only the quantity of data but also might have an impact on the quality and relevance of data.

To support the new types of interaction, we must advance data modeling, information organization, and knowledge representation for an integrated discovery and leaning system to ensure the rigor and relevance of data curation.

However, although these data could influence their clinical decisions and management, the validity and relevance of data processing methods, in particular in case of complex data sets (i.e. so-called big data, see Table 1 for related terminology) remain to be defined.

Data were analyzed to evaluate intra-platform reproducibility, inter-platform reliability and relevance of data produced on each platform for construction of a predictive model for skin sensitization in the GARD assay.

Depending on the quantity and quality and relevance of the data available, such data can be considered in a qualitative way or in a quantitative way, ranging from a listing of the costs to a modelling exercise [ 24, 25].

∘ Depending on the quantity and quality and relevance of the data available, such data can be considered in a qualitative way or in a quantitative way, ranging from a listing of the costs to a modelling exercise.

This report builds on these previous reports but refines the assessment with a WoE approach that includes an evaluation of the strength of evidence and relevance of the data to classification of CPY as a POP or PBT.

The criteria are based on the four methods evaluated in this study and the OECD reporting requirements, and have been further developed to include both reliability and relevance of test data.

The volume, inconsistency and relevance of raw data needs to be addressed, as does the ability to make it contextual – but without applying human bias to make the data fit or support a preconceived assumption or forecast.