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A set of data can be large and difficult to make any sense of and something like an average can give a simple, quick summary.
The input data is usually of negligible size (fieldline F6 being the largest at ca. 11MB) but the output data can be large (2GB in the case of F6).
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In addition to the data imbalance problem, HTS datasets can be large, containing test results for hundreds of thousands of chemical samples.
Still, rare molecular species are certainly subject to stochastic fluctuations in sequence data sets and these fluctuations can be large components of the total signal in cases where the counts of individual species are small.
We extend these by showing the under-estimation of hypertension for self-reported data can be even larger in small communities.
However, using these high resolution land cover change data can be difficult for large regional C assessments.
Even for the same samples, multiple levels of omics data can be measured in large scale.
Survey based measures are also relatively easy to administer; data can be obtained from large samples relatively quickly, and can be inexpensive.
This data can be derived from large simple implementation trials, registry entries, or population health surveys, acknowledging the loss of evidence strength.
The observed synteny between melon paired-BES and six other plant genomes showed that useful comparative genomic data can be derived through large scale BAC-end sequencing by anchoring a small proportion of the melon genome to other sequenced genomes.
If these data can be confirmed in larger COPD cohorts, it would emphasize the value of sputum markers in clinical trials and support the assumption that an anti-inflammatory treatment can have long term benefits in COPD.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com