Data quality evaluation: evaluating the suitability of data to the task at hand in different stages of the data preparation pipelines (i.e., before and after data preprocessing, after data analysis) to:

1) evaluate the uncertainty of the results;

2) classify pipeline improvements, towards methods for suggesting and minimizing human efforts in data preparation and profiling;

3) providing a data quality assurance process for scientific data characterized by imprecise values and structures.