Sentence examples for both of our datasets from inspiring English sources

Given that both of our datasets have positive rank correlations for offline and Twitter data, now-casting seems feasible and we proceed to build a prediction model.

Both of our datasets have no systematic mass error, see Fig. 11 for the GNPS dataset; for datasets that show a systematic mass error, we expect worse identification rates for the naïve method.

The majority of ta-siRNAs sequenced in both of our datasets were derived from TAS1a,b,c and TAS2 loci (Table S1).

We found that for both of our datasets, the same parameter off = +0.02 leads to excellent results.

> -wrap-foot> Having established that both of our datasets required correction for confounders and that the LMM with our chosen background genetic similarity matrix,, sufficiently corrected for confounders, we next applied FaST-LMM-Set, using the same LMM-correcting component as in the univariate test.

Table 1 shows a summary of our datasets.

We begin by attempting to reconstruct both rankings on the basis of our dataset by calculating and sorting the scores according to the approach described above.

The discrepancies between parametric and nonparametric simulations are discussed in light of our dataset and known attributes of both approaches.

Figure 6 Preview of our dataset videos.

This reduces the size of our dataset by 73%.

The statistics of our dataset is summarized in Table 2.