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One must be careful in assuming that any large dataset is a big data.
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Experience with analysis of large datasets is a big plus.
To improve the standardisation and comparability of economic evaluations among different physical activity programmes and among countries, high methodological quality and explicit reporting of a minimal dataset are important, which is a big challenge for health economists.
The Data Collector is a big data dataset consisting of 602 million call detail/messaging records growing by approximately 8 million records per month.
Data is a commodity, just because the dataset is big, does not mean the feature-set needs to be big.
Moreover, the statistical power of this methodology may be significantly reduced when the training dataset is not big enough.
For instance, the overlap between GAP's high-scoring predictions and the positive gold standard dataset is significantly bigger than the overlap with the random set (hypergeometric p-value ≈ 0.0).
Basically, that's a big win for the Free Our Data campaign; as are all the other non-personal datasets being released.
This is probably because our Drosophila training dataset is bigger than the one used by Zhang et al. (composed of 987 sequences), whereas our human training dataset is smaller than the one used by Zhang et al. (composed of 32 046 sequences).
By contrast, a big dataset is designed to be re-used for many purposes, and to answer multiple questions including questions that cannot be anticipated at the time of data collection.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com