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Complete macroinvertebrate datasets for calculating EPT or HBI were available for 23 watersheds.
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From those data we defined a minimum dataset for calculating an additive soil physical quality index (SPQI).
The dataset used for calculating the CS included proteins (i) that are annotated to be in the nuclear or cytoplasmic compartment and (ii) whose motif match is found in a disordered/unstructured region according to the IUPred prediction.
Here, the textual datasets are mainly used for calculating the text-based similarity scores between entities for controlling the quiz difficulty levels.
In addition to using our data to understand possible OTEs, as we provide all our secondary data, these datasets could prove useful for calculating false-positive and false-negative rates based on different statistical analysis techniques.
A soil database with information on modal soils expresses the degree of drainage, texture and topsoil TOC in each of the mapping units, and we can thereby determine which parameters the bounding map units differ on, and in that way also which dataset should be used for calculating the boundary width.
For correlation analysis, all datasets were calculated for correlation efficiency and were considered significant at p<0.05.
For each dataset, the performance of each method was ranked from 1 through 4, and the average of these ranks over all datasets for each procedure calculated.
For cross-referencing of RNA-seq and ChIP-seq datasets, FPKM values for each RNAseq dataset were calculated for each gene and then log normalized to a 0 to 10 point scale, with 10 representing the smallest value that captured 95% of the data.
For both datasets, we calculate coexpressions using MMC, traditional Pearson and SVA corrected Pearson.
Codon usage fractions for individual datasets were calculated as percentage of the respective total amount of counted codons.
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