Her research focuses on data provenance, causality, explanations, data quality, and algorithmic fairness.
These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature.
Nevertheless, there are some interesting issues in conclusions, such as Beijing suffering the worst data quality, which requires interpretations and explanations.
One might question data quality, but this seems the least likely explanation for the findings, given the consistently observed advantage for Chilean pregnancies and the resources and attention devoted to data integrity in Chile.
They can identify the most suitable data fields for analysis which improves data quality, and they can support interpretation of syndromic surveillance results by giving explanations for signals.
An explanation, besides a chance finding, may be that data quality on exposure for the cases and oral cancer control subjects may have differed from that of the population control subjects.
However, the most likely explanation for the brain low (or zero) IVIM values lies in the poor data quality of these measurements.
However, this explanation does not seem to be particularly plausible given that ENCODE imposes strict data quality requirements, especially with regard to tier 1 cell lines to which GM12878 belongs.
Poor data quality, plus factors such as the proportion of patients treated by these physicians in nursing homes or hospices are likely explanations for most of these additional alarms.
However, heterogeneous data quality, deviating health care standards, or differences in the distribution of competing and synergistic risk factors should be considered as alternative explanations.
