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Once Morwilog is built, we need a dataset accomplishing a certain number of characteristics for testing the system: Firstly, the feedback provided by the operator has to be simulated during the experiments.
To critically assess the merit of the All Star Game rosters, we need a dataset that contains the relevant baseball statistics.
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In order to detect signals in nucleotide sequences that might be important in controlling OR transcription, we needed a dataset of well-defined OR transcription start sites (TSSs).
You would need a big dataset about a person that's all interconnected, rather than shredding it into pieces [to make it anonymous] and losing information.
Therefore, they need a robust dataset to sell ads against.
If we want to discuss the error values more precisely, we need a larger dataset and must use the simultaneous estimation method.
With the growing data science trend, we always need a large-scale dataset to efficiently solve a problem.
Since ENSO occurs only once every couple of years, we need a sufficiently long dataset to be able to examine possible ENSO signatures in the thermosphere.
Ideally, to empirically evaluate and compare these identity resolution techniques, we need a real-world dataset in which duplicate identity records exist.
However, these results need a larger, multicentered dataset to be validated.
To test the possibility of using phenotype probabilities to predict disease causal genes linked to GWAS SNPs, we need a Gold Standard Positive (GSP) dataset to quantify its performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com