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Using "degree days" in your search should bring up both of the datasets we talked about here.
Before testing for possible mood state-related changes in the functional connectivity pattern of the five common RSNs, we examined the relative stability of this measurement by performing a simple split-half analysis of both of the datasets, repeating the procedure of the original group ICAs.
Among all SWEET genes, GmSWEET21 and GmSWEET24 showed the highest expression in both of the datasets (Fig. 5a).
Both of the datasets were used to compare and evaluate the performance of all three different methods.
Subsequent studies revisited both of the datasets described earlier (an excellent demonstration of the utility of making microarray data publicly available) and were able to reanalyze the data.
Both of the datasets used in Herskovic et al. (2007) [ 29] and Doğan et al. (2009) [ 34] are publicly available from the NLM.
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Further with the help of t-test, we calculated the difference of means of both the datasets for each of 400 dipeptides.
In both of these datasets, the Ewing's sarcoma samples were enriched for the zebrafish upregulated gene set; the Miyagawa dataset was also enriched for the downregulated gene set (Table 2).
We find that on the SRDS1 versions of the datasets, both the LA and the CTD methods significantly outperform the 5-spectrum kernel and that there are no statistically significant differences between the LA kernel and the CTD classifier.
A comparison of these datasets is provided in Additional file 7. Briefly, 249 matched both of the other datasets and 237 matched one of the other datasets.
Figure 3 Showing the results of pharmacophore based screening of both the datasets.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com