Such data are difficult to obtain because of the practical problems of applying a pure moment.
Despite their high taxonomic accuracy, both types of data are difficult to extrapolate to landscape scales.
Problems arise in formalising such linkages because substantial spatial and longitudinal whole-farm data are difficult to acquire.
This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques.
Since melting point data are difficult to obtain without making the actual salt, predicting salt solubility remains difficult.
Seizure frequency data are difficult to work with because they are so variable both between and within patients.
Functional data are difficult to manage for most classical statistical techniques, given the very high (or intrinsically infinite) dimensionality.
However, researchers concede these data are difficult to parse from other tobacco control tactics such as graphic warnings, enacted around the same time.
Although complication rates did not significantly differ between groups, these data are difficult to interpret given the heterogeneity in reporting complications between studies.
The results obtained are fairly good, showing that the generalized correlations are convenient specifically for those compounds for which experimental data are difficult to measure.
When field data are difficult to obtain, an expert knowledge-based model can be used as a replacement for the data-based methods.
