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However, in recent years the size of datasets is growing steeply; the existing window function implementations are not efficient enough.
In addition, the number of CLIP-seq datasets is growing (Fig. 1).
Meanwhile a new generation of journals, dedicated to publishing datasets, is growing fast.
Uniform retrospective quality assessment is resource-intensive and will not be practically feasible because the number of ChIP-seq datasets is growing exponentially.
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With the costs of genotyping dropping continuously, and with the expected use of whole-genome sequence data in practical applications in the short term [ 2], the dimensions of datasets are growing rapidly.
In contrast, the plants for the Accession II dataset were grown under long-days and harvested long before flowering.
The plants for the Accession I dataset were grown under short-days and harvested just prior to flowering onset for some of the accessions.
Propelled by a fast evolving landscape of techniques and datasets, data science is growing rapidly.
This resource is growing as new datasets are released and processed for inclusion.
The dataset is also now much larger (4-fold increase, with about 1500 insertions in July 2012), and is growing steadily.
Specifically, it is growing the use of data mining techniques to solve biological problems by analyzing large biological datasets [ 1].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com