| Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models M Ryo, B Angelov, S Mammola, JM Kass, BM Benito, F Hartig Ecography 44 (2), 199-205, 2021 | 88 | 2021 |
| sdmbench: R package for benchmarking species distribution models B Angelov Journal of Open Source Software 3 (29), 847, 2018 | 7 | 2018 |
| Review of species distribution modeling opensource software B Angelov Researchgate doi 10, 2019 | 3 | 2019 |
| species2vec: A novel method for species representation B Angelov BioRxiv, 461996, 2018 | 2 | 2018 |
| Improving the interpretability of species distribution models by using local approximations B Angelov bioRxiv, 454991, 2018 | 1 | 2018 |
| The Study of Progress: Entropy, Complexity, and the Great Filter B Angelov OSF Preprints, 2023 | | 2023 |
| Managing the century of complexity: origins, evolution and productive future avenues with systems thinking B Angelov OSF Preprints, 2022 | | 2022 |
| Research Data Strategy: framework and motivating factors B Angelov OSF Preprints, 2020 | | 2020 |
| Mud Volcanoes: A Window to the Deep Biosphere. Investigating succession and functional shifts in marine deep subsurface microbial communities exposed to mud volcanism B Angelov University of Bremen Bremen/Germany, 2013 | | 2013 |
Masahiro RyoProfessor of Environmental Data Science, Leibniz Centre for Agricultural Landscape Research確認したメール アドレス: zalf.de
