Thus, the conceptual framework follows the typical steps of conducting a model-based ESS: collection and preparation of empirical data for the model-based data processing, assumption-making, model application and preparation of model outputs as well as deriving comprehensive conclusions and recommendations.
We stress that documentation of data processing, assumptions and limitations is the most important aspect of function derivation; documentation provides transparency and allows others to update functions more easily.
It is often unclear what a count truly means, physically or physiologically, as the underlying data processing method and assumptions are often concealed from the end user.
Most of the data processing methods make this assumption although some standardization methods also exist that utilize probe and RNA/DNA sequence information in order to reduce the signal differences resulting from sub-optimal amplification and hybridization conditions that affect gene expression estimates to a varying degree [ 89, 90].
Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing.
This variability most likely lies in data interpretation models that incorporate different assumptions during data processing and widespread problems of overfitting high dimensional data with an extremely large number of molecular measurements relative to limited sample size [ 19].
As there is no complete information about both nanoparticle and substrate geometry at these parts of AFM image, all the data processing algorithms need to make some assumptions regarding nanoparticle and substrate properties.
Data processing by these systems does not require assumptions of how outputs relate to inputs.
Data analysis results need to be interpreted by decision makers, and this may require users to be able to analyze the assumptions at each stage of data processing and possibly re-trace the analysis steps.
The geopotential height systematic error, which results from the measurements and from assumptions made in the RO data processing, was estimated to no more than 7 m in the UTLS [ Scherllin-Pirscher et al., 2011].
In ordinary mRNA expression microarray, it is a standard data processing procedure to normalize the microarray data with an assumption that the whole mRNA expression signal is constant among the samples.
