A major limitation of the present study was the limited amount of qualitative data extracted from the child health checkup files and workshop discussion notes.
A comparison of Fig. 6 and Fig. 1 shows that by constructing TF activities using a preliminary TRN, we significantly increase the amount of information extracted from expression data.
The UK Food Standards Agency Nutrient Databank was used to determine the carotenoid content of foods; in addition, a limited amount of data was extracted from the US Department of Agriculture National Nutrient Database.
A large amount of data was extracted from the identified studies, including but not limited to study type, sample size, gender, age distribution, risk estimates with confidence intervals, and adjustment variables (see Additional file 1).
The challenge lies very often in the analysis of a huge amount of data to extract meaningful information and use them to answer some of the fundamental biological questions.
Our analysis does not attempt to replace any existing method and the point here is the possibility to evaluate a great amount of data and extract information from it.
If more than one paper provided results of the same study population, data were first extracted from the article with the greatest amount of information, and supplementary data extracted from the other papers if required.
The results show that a significant amount of data can be extracted from flotation surface froths.
We have no information about the recall but it is clear that a large amount of data has been extracted.
In recent years, the plant phenotyping research has seen the emergence of high-throughput plant screening facilities [ 1, 6]; however, few image and mesh processing solutions are available to analyse the large amount of data captured and extract yield determinants (i.e. plant, leaf, or root characteristics).
