Big datasets are becoming more prevalent in modern statistics.
With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas.
Composite indicators in population health datasets are becoming more common.
This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available.
Fortunately, with the increasing use of HRQoL outcomes in research, historical datasets are becoming more readily available.
Such datasets are becoming more and more useful and versatile with the growing sophistication and availability of 3D image viewing, manipulation, and analysis software.
Experimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis.
These datasets are becoming available for more miRNAs and their integration into functional annotation pipelines, such as the one in our study, will be highly informative in identifying functional miRNA-mRNA interactions.
In this context, research challenges that provide access to a large number of research teams to the same dataset are becoming a valuable framework to advance the state of the art in the field and to sustain the process of reproducibility needed by the scientific community.
Given the substantial value of these accumulated datasets, it is becoming increasingly common to reuse gene expression data to validate new findings or to pose new biological questions.
