DOI: 10.6084/m9.figshare.30610085.v1
title: Data associated with manuscript

description: Data associated with main analysis and figures presented in manuscript titled: "Sustainable soil management practices are associated with increases in crop defense through soil microbiome changes." For raw unprocessed microbiome data see NCBI SRA Accession: PRJNA1334013. Our bioinformatics were performed in AMPtk and R as described in the manuscript. For synthetic analysis we recommend returning to these source data. What follows are processed data for the downstream analyses conducted in the main manuscript, column and object definitions are given in the file named "Metadata_ColumnNamesAndDefinions.csv" and joins for datasets along with sub-setting from the full repository of raw data at NCBI can be conducted with "Metadata_JoinsAndSampleInformation.csv" which also includes additional metadata on samples not found elsewhere in analytical files. Data associated with Figure 1a, which include locations for sites, cannot be released due to privacy concerns for participants. Data to construct Figure 1b were previously published at https://doi.org/10.6084/m9.figshare.25146026.v1 for the manuscript titled: "Motivating organic farmers to adopt practices that support the pest-suppressive microbiome relies on understanding their beliefs." To recreate the relative abundance plots, use "Data_BacteriaRelativeAbundance.rds", "Data_FungiRelativeAbundance.rds", and "KeyAnalyticalScript_Figure1c-d.r" to produce Figure 1c-d. These scripts are applicable to new datasets and were adapted from prior workflows. For the statistical analysis underlying Figure 1e-g, see "Data_JAAndCluster.csv", "Data_SAAndCluster.csv", "Data_ProgenyAndCluster.csv" along with "KeyAnalyticalScript_Figure1e-g.r" which can be run to produce the numerical results. Datasets titled "Data_BetaDiversityAndPractices.csv" and "Data_AlphaDiversityAndPractices.csv" are used by the script titled "KeyAnalyticalScript_Figure2a.r" which details the general analytical approach for machine learning used at various steps leading up to structural equation model construction. Here we give an example with the models and statistics used to display alpha diversity measures in Figure 2a. For Figure 2b, the datasets titled "Data_BacteriaPrevFiltDifferentialAbundance.rds" and "Data_FungiPrevFiltDifferentialAbundance.rds" are used by "KeyAnalyticalScript_Figure2b.r" which demonstrates our general differential abundance modeling approach. These data have been modified including via prevalence filtering as described in the manuscript. Data generated by these analyses are shared in Supplementary Table 3. Datasets titled "Data_BetaDiversityAndPhytohormones.csv" and "Data_SpecificTaxaAndPhytohormones.csv" are used by the script titled "KeyAnalyticalScript_Figure3.r" which collectively detail the models and statistics displayed in Figure 3. Datasets titled "Data_BetaDiversityStructuralEquationModel.csv" and "Data_SpecificTaxaStructuralEquationModel.csv" are used by the script titled "KeyAnalyticalScript_Figure4.r" which collectively detail the analysis displayed in Figure 4, and Supplementary Tables 4 and 5. Intermediary datasets and scripts, including those displayed in the Supplementary Information are available upon request.