Data associated with manuscript

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.

本数据集对应题为《可持续土壤管理措施通过土壤微生物组（soil microbiome）改变提升作物防御能力》的手稿中主要分析与图表所关联的数据。原始未处理的微生物组数据可在国家生物技术信息中心序列读取档案（NCBI SRA）中获取，登录号为PRJNA1334013。本研究的生物信息学分析依托AMPtk与R软件完成，具体方法详见手稿。如需开展合成分析，建议回溯获取上述源数据。
以下为主手稿中后续分析所用的已处理数据，列与对象的定义详见名为"Metadata_ColumnNamesAndDefinions.csv"的文件。数据集的关联、子集提取可借助"Metadata_JoinsAndSampleInformation.csv"完成，该文件同时收录了其他分析文件未涵盖的样本额外元数据（metadata）。
与图1a相关的数据（含采样点位置）因涉及研究对象的隐私保护要求，无法公开。用于构建图1b的数据已在题为《通过理解有机种植户的信念，推动其采用支持害虫抑制性微生物组的种植措施》的手稿中发表，公开链接为https://doi.org/10.6084/m9.figshare.25146026.v1。
如需复现相对丰度（relative abundance）绘图，可使用"Data_BacteriaRelativeAbundance.rds"、"Data_FungiRelativeAbundance.rds"以及"KeyAnalyticalScript_Figure1c-d.r"生成图1c与图1d。上述脚本适配全新数据集，改编自此前的分析流程。
针对图1e至图1g的统计分析，可使用"Data_JAAndCluster.csv"、"Data_SAAndCluster.csv"、"Data_ProgenyAndCluster.csv"配合"KeyAnalyticalScript_Figure1e-g.r"运行以生成数值结果。
数据集"Data_BetaDiversityAndPractices.csv"（β多样性（beta diversity）与管理措施）与"Data_AlphaDiversityAndPractices.csv"（α多样性（alpha diversity）与管理措施）将被用于"KeyAnalyticalScript_Figure2a.r"脚本，该脚本详细阐述了在构建结构方程模型（structural equation model）前各步骤所用到的机器学习（machine learning）通用分析方法。此处以图2a中展示的α多样性测度所用的模型与统计方法为例进行说明。
针对图2b，需使用数据集"Data_BacteriaPrevFiltDifferentialAbundance.rds"与"Data_FungiPrevFiltDifferentialAbundance.rds"配合"KeyAnalyticalScript_Figure2b.r"脚本，以展示本研究的差异丰度建模（differential abundance modeling）通用方法。上述数据已按照手稿所述流程进行了包括prevalence过滤（prevalence filtering）在内的预处理修改。本分析生成的数据已共享于补充表3。
数据集"Data_BetaDiversityAndPhytohormones.csv"（β多样性与植物激素（phytohormones））与"Data_SpecificTaxaAndPhytohormones.csv"（特定类群与植物激素）将被用于"KeyAnalyticalScript_Figure3.r"脚本，该脚本详细阐述了图3所展示的模型与统计方法。
数据集"Data_BetaDiversityStructuralEquationModel.csv"（β多样性与结构方程模型）与"Data_SpecificTaxaStructuralEquationModel.csv"（特定类群与结构方程模型）将被用于"KeyAnalyticalScript_Figure4.r"脚本，该脚本详细阐述了图4以及补充表4、5所展示的分析内容。
包括补充信息中所列的中间数据集与脚本均可按需索取。