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Information on the datasets used in this study.
The environmental datasets used in this study are publicly available online as stated in the methods.
Publicly available RNA-seq datasets used in this study were downloaded from ENCODE (Supplementary Table 5).
The datasets used in this study are available at https://data.matr.io/1.
Table 1 Composition of HMP and HGTree derived datasets used in this study.
The soil datasets used in this study are also publicly available through the USDA-NRCS NCSS data repository, http://ncsslabdatamart.sc.egov.usda.gov/.sc.egov.usda.gov/
Publicly available Repli-Seq datasets used in this study were downloaded from ENCODE data portal (https://www.encodeproject.org/matrix/ type=Experiment&assay_title=Repli-seq).org/matrix/ type=Experiment&assay_title=Repli-seq
Our method is not specific to the datasets used in this study and users can train and test the deep learning model on any relevant imaging data.
The global datasets used in this study include daily surface meteorology records, satellite remote sensing data for driving the ET and PET algorithms, and precipitation and discharge records for the water balance analyses and ET validation.
The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.edu.cn/bioinf/TargetGDrug
Table 1 Number of trees by diameter class in three datasets used in this study.
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