## Dataset

- 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>

- 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>

- ***For a detailed description of this dataset, please refer to the methods section of the paper.***

**Dataset file structure directory**

```
├─test_set
│      New_test_values.csv
│      test_G.csv
│      test_GE.csv
│
└─train_set
       G.csv
       GE.csv
       New_Yield_values.csv
       train_Yield_folds.csv
```

**Description**

**train_set**
- ***G.csv***<br>
Genome-wide principal component data used to train the G2P model.
- ***GE.csv***<br>
The data was integrated from genome-wide principal component data, weather data and soil data.
- ***train_Yield_folds.csv***<br>
The dataset is a ten-fold cross-validation dataset generated by the kfolds.py script for model training and testing.<br>
- ***New_Yield_values.csv***
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>

**test_set**
- ***test_G.csv***
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>
- ***test_GE.csv***
This dataset was integrated from genotype and environment to validate the predictive performance of the model across environments under environmental stress.<br>
- ***New_test_values.csv***
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.

license:apache-2.0 region:us
{"license": "apache-2.0"}
TAGS
#license-apache-2.0 #region-us 

## Dataset

- For the present study, we used data from the GxE competition advocated by the G2F project in 2022 (URL 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>

- 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>

- *For a detailed description of this dataset, please refer to the methods section of the paper.*

Dataset file structure directory



Description

train_set
- *G.csv*<br>
Genome-wide principal component data used to train the G2P model.
- *URL*<br>
The data was integrated from genome-wide principal component data, weather data and soil data.
- *train_Yield_folds.csv*<br>
The dataset is a ten-fold cross-validation dataset generated by the URL script for model training and testing.<br>
- *New_Yield_values.csv*
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>

test_set
- *test_G.csv*
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>
- *test_GE.csv*
This dataset was integrated from genotype and environment to validate the predictive performance of the model across environments under environmental stress.<br>
- *New_test_values.csv*
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.

## Dataset

- For the present study, we used data from the GxE competition advocated by the G2F project in 2022 (URL 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>

- 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>

- *For a detailed description of this dataset, please refer to the methods section of the paper.*

Dataset file structure directory



Description

train_set
- *G.csv*<br>
Genome-wide principal component data used to train the G2P model.
- *URL*<br>
The data was integrated from genome-wide principal component data, weather data and soil data.
- *train_Yield_folds.csv*<br>
The dataset is a ten-fold cross-validation dataset generated by the URL script for model training and testing.<br>
- *New_Yield_values.csv*
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>

test_set
- *test_G.csv*
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>
- *test_GE.csv*
This dataset was integrated from genotype and environment to validate the predictive performance of the model across environments under environmental stress.<br>
- *New_test_values.csv*
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.
TAGS
#license-apache-2.0 #region-us 
 ## Dataset

- For the present study, we used data from the GxE competition advocated by the G2F project in 2022 (URL 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>

- 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>

- *For a detailed description of this dataset, please refer to the methods section of the paper.*

Dataset file structure directory



Description

train_set
- *G.csv*<br>
Genome-wide principal component data used to train the G2P model.
- *URL*<br>
The data was integrated from genome-wide principal component data, weather data and soil data.
- *train_Yield_folds.csv*<br>
The dataset is a ten-fold cross-validation dataset generated by the URL script for model training and testing.<br>
- *New_Yield_values.csv*
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>

test_set
- *test_G.csv*
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>
- *test_GE.csv*
This dataset was integrated from genotype and environment to validate the predictive performance of the model across environments under environmental stress.<br>
- *New_test_values.csv*
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.
passage: TAGS
#license-apache-2.0 #region-us 
