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 | 123 | 2021 |
sdmbench: R package for benchmarking species distribution models B Angelov Journal of Open Source Software 3 (29), 847, 2018 | 8 | 2018 |
Review of species distribution modeling opensource software B Angelov Researchgate doi 10, 2019 | 4 | 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 |
The Enabling Data Model and Manifesto B Angelov, D Castro-Gavino OSF, 0 | | |