Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
|
| Online Access: | https://doi.org/10.5281/zenodo.18890755 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901747790774272 |
|---|---|
| author | peng, fan chen, ling wen, yi |
| author_facet | peng, fan chen, ling wen, yi |
| contents | <p>This dataset is a comprehensive collection of all supporting data and reproducible experimental materials for the paper <em>“Predicting Productivity and Climatic Resilience of Coniferous–Broadleaved Mixed Plantations with 3-PGmix and Machine Learning.”</em> It includes multi-source data used for model inputs, machine-learning training, and result mapping, covering cartographic (mapping) data, soil and climate data, and machine-learning training datasets. The dataset is intended to support the reproducibility of the paper’s findings and promote transparent data sharing, enabling researchers to conduct comparative studies, reproduce the methods, or extend the analyses in the same or similar regions.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18890755 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | data_ECOINF-D-25-03375 peng, fan chen, ling wen, yi <p>This dataset is a comprehensive collection of all supporting data and reproducible experimental materials for the paper <em>“Predicting Productivity and Climatic Resilience of Coniferous–Broadleaved Mixed Plantations with 3-PGmix and Machine Learning.”</em> It includes multi-source data used for model inputs, machine-learning training, and result mapping, covering cartographic (mapping) data, soil and climate data, and machine-learning training datasets. The dataset is intended to support the reproducibility of the paper’s findings and promote transparent data sharing, enabling researchers to conduct comparative studies, reproduce the methods, or extend the analyses in the same or similar regions.</p> |
| title | data_ECOINF-D-25-03375 |
| url | https://doi.org/10.5281/zenodo.18890755 |