PhenoFormer Dataset

Companion Dataset for the Article  "Deep Learning Meets Tree Phenology Modeling: PhenoFormer vs. Process-Based Models"  Garnot et al., 2025  

This archive contains the dataset used in the numerical experiments for the article. It includes:  - Phenological observations from the Swiss Phenology Network for 9 woody plant species across 175 sites over 70 years.  - Daily meteorological variables from the DaymetCH dataset for each observation site and year.  

Dataset format:  - learning-models-data – Formatted for use with Python code for deep learning and machine learning models.  - process-models-data – Formatted for use with R code for process-based models.  

Code repository:  https://github.com/VSainteuf/PhenoFormer  

Citation:  @article{phenoformer,    title={Deep learning meets tree phenology modeling: PhenoFormer vs. process-based models},    author={Garnot, Vivien Sainte Fare and Spafford, Lynsay and Lever, Jelle and   Sigg, Christian and Pietragalla, Barbara and Vitasse, Yann   and Gessler, Arthur and Wegner, Jan Dirk},    journal={{Methods in Ecology and Evolution}},    year={2025}  }  

Data source and processing:  - Data source: Federal Office of Meteorology and Climatology (MeteoSwiss)  - Meteorological data processing: Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)