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Evaluation of data relevance, reliability and contribution to uncertainty is crucial in regulatory health risk assessment if robust conclusions are to be drawn.
Various reports make recommendations for improving the quality of risk assessments and different criteria catalogues have been published to support evaluation of data relevance and reliability.
The concept that bridges the gap between the data and the model is called Domain of Data Relevance and corresponds to a physical area in the network where a given piece of data is relevant.
Each level of data relevance will be associated to a transmission service, as presented in Table 1.Packets carrying critical data will always be retransmitted if corrupted.
Moreover, we expect that transmission of auxiliary data is not mandatory and some WSN may not have this kind of data relevance.
In combination with the so-called CRED criteria for evaluation of data relevance (published by Moermond et al. 56]), an overall evaluation of data adequacy for nanomaterial ecotoxicity studies can be made.
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To enhance data relevance, consideration of those potential confounders would require prospective registration of gender-specific parameters.
Highly adaptable CEOs regularly plug into broad information flows: They scan wide networks and diverse sources of data, finding relevance in information that may at first seem unrelated to their businesses.
The vast majority of data supporting the relevance of α-MSH/MC-1R signaling for the immunomodulatory effects of α-MSH have been carried out in vitro.
We also identified problems of data validity and relevance in studying inequalities.
The first indications of data utility and relevance will be identified during the development of the EI.
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