CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (v2.0)
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CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2021. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 79,122 phenotype measurements, 378 genotypes of maize lines, environmental data of 178 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (<em>T<sub>min</sub></em>)<em>,</em> average temperature (<em>T<sub>mean</sub></em>)<em>, </em>maximum temperature (<em>T<sub>max</sub></em>)<em>,</em> minimum dew point (<em>DP<sub>min</sub></em>)<em>,</em> average dew point (<em>DP<sub>mean</sub></em>)<em>, </em>maximum dew point (<em>DP<sub>max</sub></em>)<em>, </em>minimum relative humidity (<em>RH<sub>min</sub></em>)<em>, </em>average relative humidity (<em>RH<sub>mean</sub></em>)<em>, </em>maximum relative humidity (<em>RH<sub>max</sub></em>)<em>, </em>minimum solar radiation (<em>SR<sub>min</sub></em>)<em>, </em>average solar radiation (<em>SR<sub>mean</sub></em>)<em>, </em>maximum solar radiation (<em>SR<sub>max</sub></em>)<em>, </em>accumulative rainfall (<em>R<sub>acc</sub></em>)<em>, </em>average wind speed (<em>WS<sub>mean</sub></em>), and average wind direction (<em>WD<sub>mean</sub></em>). This package also contains the raw G2F data and preprocessing pipeline.
CLIM4OMICS分析与数据库是G2F数据仓储的改良版数据库，涵盖2014-2021年间美国84个试验田与加拿大安大略省（ON）范围内用于玉米产量可预测性研究的组学（OMICs，含遗传与表型数据）及环境数据。本数据流程的目标为整合、优化并合成多维度G2F数据，涵盖用于基因型-环境互作（Genotype by Environment, GxE）建模的基因型、表型与环境数据。本数据集包含79,122个表型测量值、378个玉米自交系基因型、178个试验位点的环境数据，以及用于质量控制（Quality Control, QC）、一致性控制（Consistency Control, CC）流程与基因型-环境互作机器学习（Machine Learning, ML）建模的Python脚本。环境数据提取自NWS、DayMet与NSRDB数据库，并经过质量控制与一致性控制处理。该环境数据集涵盖：最低气温（<em>T<sub>min</sub></em>）、平均气温（<em>T<sub>mean</sub></em>）、最高气温（<em>T<sub>max</sub></em>）、最低露点温度（<em>DP<sub>min</sub></em>）、平均露点温度（<em>DP<sub>mean</sub></em>）、最高露点温度（<em>DP<sub>max</sub></em>）、最低相对湿度（<em>RH<sub>min</sub></em>）、平均相对湿度（<em>RH<sub>mean</sub></em>）、最高相对湿度（<em>RH<sub>max</sub></em>）、最低太阳辐射（<em>SR<sub>min</sub></em>）、平均太阳辐射（<em>SR<sub>mean</sub></em>）、最高太阳辐射（<em>SR<sub>max</sub></em>）、累积降雨量（<em>R<sub>acc</sub></em>）、平均风速（<em>WS<sub>mean</sub></em>）以及平均风向（<em>WD<sub>mean</sub></em>）。该数据包还包含原始G2F数据与预处理流程。
