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This rare opportunity to work directly with the statues resulted in a valuable dataset summarizing their dynamic characteristics for museum engineers and curators.
The Amenity Space, was built by using a dataset summarizing the precise location of millions of amenities, introducing a clustering algorithm to identify neighborhoods, and then using the identified neighborhoods to map the probability that two amenities will be co-located in one of them.
The resulting dataset, summarizing the G values and the corresponding experimental conditions in which they where collected, is presented in Table S1 (see Supplementary Information).
We drew North American occurrence data from the U.S. Department of Agriculture's Plants Database [54], summarizing known ranges of each species at the level of counties in which they are known to occur by means of adding fields for each species to the attributes table of a vector dataset summarizing boundaries of 3111 counties across the United States.
To evaluate the proposed method, we applied the GP predictor to a meta dataset summarizing major G-quadruplex experiment data available as of today.
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The resulting dataset summarizes the effects of different GAGs on the aggregation kinetics of different proteins, together with the precise experimental conditions in which these effects were recorded in each case (GAG and protein types and concentrations; composition, ionic strength, total solute concentration, pH and temperature of the milieu).
Table S6, with allele frequencies for individual populations, regional groups, and the complete dataset summarized in Supp.
The eight selected empirical datasets, summarized in Table 3, are the following.
The reviewer is correct that the graphs in Figure 2B-E present quantification of the datasets summarized in Figure 2A.
Unlike the datasets summarized in table 2, the sequences from the mother infant gut samples of the Vallés et al. study were unassembled.
The protein specific features were generated using the datasets summarized in Table 1 and an additional dataset (Qi et al., 2006), from which 20 gene expression attributes were obtained.
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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