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license: apache-2.0 |
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--- |
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## Dataset |
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- For the present study, we used data from the GxE competition advocated by the G2F project in 2022 (https://www.maizegxeprediction2022.org/), including genetic markers (G2F-G) for maize inbred lines, phenotypic measurements (G2F-P) collected throughout each growing season, metadata (G2F-M) for each field trial, environmental covariate (EC) data, and environmental (G2F-E) data. G2F-E data were mainly climatic and soil variables captured during crop development in each experimental trial.<br> |
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- In order to explore the influence of environmental factors on yield prediction results, we designed two sets of prediction scenarios: **1)** yield prediction based on the whole genome, and **2)** yield prediction integrating genome, weather and soil factors. Different data sets are generated for different prediction scenarios.<br> |
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- ***For a detailed description of this dataset, please refer to the methods section of the paper.*** |
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**Dataset file structure directory** |
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``` |
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├─test_set |
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│ New_test_values.csv |
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│ test_G.csv |
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│ test_GE.csv |
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│ |
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└─train_set |
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G.csv |
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GE.csv |
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New_Yield_values.csv |
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train_Yield_folds.csv |
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``` |
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**Description** |
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**train_set** |
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- ***G.csv***<br> |
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Genome-wide principal component data used to train the G2P model. |
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- ***GE.csv***<br> |
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The data was integrated from genome-wide principal component data, weather data and soil data. |
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- ***train_Yield_folds.csv***<br> |
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The dataset is a ten-fold cross-validation dataset generated by the kfolds.py script for model training and testing.<br> |
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- ***New_Yield_values.csv*** |
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This dataset is assembled from the base model predictions and is primarily used to train the second layer of models in the stacking framework.<br> |
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**test_set** |
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- ***test_G.csv*** |
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This dataset is a predicted population of target genotypes from an untested environment and is used to validate the predictive performance of the model when environmental effects are ignored.<br> |
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- ***test_GE.csv*** |
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This dataset was integrated from genotype and environment to validate the predictive performance of the model across environments under environmental stress.<br> |
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- ***New_test_values.csv*** |
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This dataset is composed of the predicted values of the base model in the new environment and is used as a prediction set for the second layer of the model in the stacking framework. |
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