Currently available data size and data quality make the reconstruction of gene networks from gene expression data a challenge.
A scoring metric allows for direct comparison of data quantity and data quality, and makes transparent the effort made by different sensor operators.
For policy makers and researchers, it is also crucial to check the data quality before making policy measures or conducting their own research.
The databases contain valuable information and improvements in data quality would make them even more useful.
This area is particularly challenging because of the difficulty of controlling these multisensory stimuli, and in the case of human neuroimaging, maintaining data quality while making movements.
The increase in the availability of bandwidth for wireless links, network integration, and the computational power on fixed and mobile platforms at affordable costs allows nowadays for the handling of audio and video data, their quality making them suitable for medical application.
Currently, when a modification is made, pre-existing manual annotations are overwritten and only basic data quality controls are made.
In particular, the assessment of big data quality should be made not just of the individual data source (e.g. OLTP systems), but also of any data that came from merging sources.
Regardless the PMU data quality issue, to make these methods applicable in practice, the detection accuracy of arrival time, the impact from the non-constant wave speed throughout the network and the adaptability to different network topologies should be first investigated and guaranteed.
This allows an initial assessment of overall data quality to be made by visualising the unfiltered data.
