This repository contains the data, generated data, and algorithm code used in our research paper. All users of this data or code are required to cite the following paper as acknowledgment of the original authors' work.Citation:Zheng Du, Bao Zhang, Yibin Yao, Qingzhi Zhao, Chaoqian Xu, Qi Zhang, Hongming Li, and Quanyu Chen. (2025). Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations. International Journal of Applied Earth Observation and Geoinformation (under review). Zheng Du, Bao Zhang, Yibin Yao, Qingzhi Zhao, Chaoqian Xu, Qi Zhang, Hongming Li, and Quanyu Chen. (2025). Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15468753 Files:This repository includes the following files:01_data/: Folder containing the raw data and generated data used in the study.02_code/: Folder with the algorithm implementation and related scripts.readme.txt: This file, containing instructions on how to use the data, code, and related information. Contact InformationFor any questions or suggestions, feel free to contact us at:Email 1:  [email protected] 2:  [email protected] We are grateful to CDSE, IGRA, and ECMWF for providing the Sentinel-3B, radiosonde, and ERA5 data. We also thank Zenodo for sharing the GNSS PWV data (https://doi.org/10.5281/zenodo.6973528). This work was supported by the Basic Science Center Program of the National Natural Science Foundation of China (42388102), the Key Research and Development Program of Guangxi Province (AB24010144), the Nanning Science and Technology Major Program (20241027), the Fundamental Research Funds for the Central Universities (2042024kf0039), and the China Postdoctoral Science Foundation (GZC20241259).

This repository contains the data, generated data, and algorithm code used in our research paper. All users of this data or code are required to cite the following paper as acknowledgment of the original authors' work.Citation:Zheng Du, Bao Zhang, Yibin Yao, Qingzhi Zhao, Chaoqian Xu, Qi Zhang, Hongming Li, and Quanyu Chen. (2025). Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations. International Journal of Applied Earth Observation and Geoinformation (under review). Zheng Du, Bao Zhang, Yibin Yao, Qingzhi Zhao, Chaoqian Xu, Qi Zhang, Hongming Li, and Quanyu Chen. (2025). Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15468753 Files:This repository includes the following files:01_data/: Folder containing the raw data and generated data used in the study.02_code/: Folder with the algorithm implementation and related scripts.readme.txt: This file, containing instructions on how to use the data, code, and related information. Contact InformationFor any questions or suggestions, feel free to contact us at:Email 1:  [email protected] 2:  [email protected] We are grateful to CDSE, IGRA, and ECMWF for providing the Sentinel-3B, radiosonde, and ERA5 data. We also thank Zenodo for sharing the GNSS PWV data (https://doi.org/10.5281/zenodo.6973528). This work was supported by the Basic Science Center Program of the National Natural Science Foundation of China (42388102), the Key Research and Development Program of Guangxi Province (AB24010144), the Nanning Science and Technology Major Program (20241027), the Fundamental Research Funds for the Central Universities (2042024kf0039), and the China Postdoctoral Science Foundation (GZC20241259).

Additional file 1.

Additional file 1.

Knowledge on species’ breeding biology is the building blocks of avian life history theory. A review for the current status of the knowledge at a global scale is needed to highlight the priority for future research. We collected all available information on three critical nesting parameters (clutch size, incubation period and nestling period) for the close to 10 000 bird species in the world and identified taxonomic, geographic and habitat gaps in the distribution of knowledge on avian breeding biology. The results show that only one third of all extant species are well known regarding the three nesting parameters analyzed, while the rest are partly or poorly known. Most data deficient taxonomic groups are tropical forest nesters, particularly from the Amazon basin, southeast Asia, Equatorial Africa and Madagascar – the places that harbor the world's highest bird diversity. These knowledge gaps could be hampering our understanding of avian life histories. Ornithologists are encouraged to pay more efforts to explore the breeding biology of those poorly-known species.