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The datasets (PSGFS_compiled_data_2022.xlsx, PSGFS_compiled_data_2023 and PSGFS_compiled_data_2024.xlsx) were collected by undergraduate students during the time they participated in the Plant Science for Global Food Security (PSGFS) program in summers 2022, 2023 and 2024 at the International Rice Research Institute (IRRI; Los Baños, Philippines). The PSGFS program is an initiative funded by the National Science Foundation (Grant: NSF IRES #2106718) and led by Diane Wang and Gary Burniske of Purdue University and Amelia Henry and Anilyn Maningas of IRRI. Purdue University PhD student, To-Chia Ting, assisted in compiling these datasets.  The explanation of each worksheet in a excel file could be found in the associated word files (PSGFS_README_2022.doc, PSGFS_README_2023.doc and PSGFS_README_2024.doc). PDF files of the presentations given by the students are also provided and compressed in the Student_presentation_2022.zip, Student_presentation_2023.zip and Student_presentation_2024.zip file. File names of the presentations are composed of worksheet names and students’ last names. 

Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants. 

PSGFS_compiled_data_2022.xlsx contains datasets collected by eight undergraduate students during the time they participated in the Plant Science for Global Food Security (PSGFS) program in Summer 2022 at the International Rice Research Institute (IRRI; Los Baños, Philippines). The PSFGS program is an initiative funded by the National Science Foundation (Grant: NSF IRES #2106718) and led by Diane Wang and Gary Burniske of Purdue University and Amelia Henry and Anilyn Maningas of IRRI. Purdue University PhD student, To-Chia Ting, assisted in compiling these datasets. </p>  </p> The explanation of each worksheet in PSGFS_compiled_data_2022.xlsx could be found at the README.doc. PDF files of the presentations given by the eight students are also provided and compressed in the Student_presentation_PDFs.zip file. File names of the presentations are composed of worksheet names and students’ last names. </p> Grants: NSF IRES, grant number: 2106718