The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, Anna Krystalli, Alexander Morley, Martin O'Reilly, and Kirstie Whitaker. The turing way: a handbook for reproducible data science. 2019. URL:, doi:10.5281/ZENODO.3233986.


Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, June 2009. URL:, doi:10.1109/cvpr.2009.5206848.


Jesper Sören Dramsch. Make Machine Learning work in the Real World. Self-published, 2021. URL:


Peter D. Dueben, Martin G. Schultz, Matthew Chantry, David John Gagne, David Matthew Hall, and Amy McGovern. Challenges and benchmark datasets for machine learning in the atmospheric sciences: definition, status, and outlook. Artificial Intelligence for the Earth Systems, July 2022. URL:, doi:10.1175/aies-d-21-0002.1.


Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. Rainbow: combining improvements in deep reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, April 2018. URL:, doi:10.1609/aaai.v32i1.11796.


Allison Marie Horst, Alison Presmanes Hill, and Kristen B Gorman. palmerpenguins: Palmer Archipelago (Antarctica) penguin data. 2020. R package version 0.1.0. URL:, doi:10.5281/zenodo.3960218.


Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, and Alexandru Coca. Alibi explain: algorithms for explaining machine learning models. Journal of Machine Learning Research, 22(181):1–7, 2021. URL:


Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. Captum: a unified and generic model interpretability library for pytorch. 2020. arXiv:2009.07896.


Michael A. Lones. How to avoid machine learning pitfalls: a guide for academic researchers. 2021. URL:, doi:10.48550/ARXIV.2108.02497.


Scott Lundberg and Su-In Lee. A unified approach to interpreting model predictions. 2017. URL:, doi:10.48550/ARXIV.1705.07874.


Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau, and Tobias Meisen. Ablation studies in artificial neural networks. 2019. URL:, doi:10.48550/ARXIV.1901.08644.


Goku Mohandas. Home - made with ml., 2022.


Christoph Molnar. Interpretable Machine Learning. Leanpub, 2 edition, 2022. URL:


F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.


Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily Fox, and Hugo Larochelle. Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program). 2020. URL:, doi:10.48550/ARXIV.2003.12206.


Sebastian Raschka. Model evaluation, model selection, and algorithm selection in machine learning. 2018. URL:, doi:10.48550/ARXIV.1811.12808.


Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, and Nils Thuerey. WeatherBench: a benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems, November 2020. URL:, doi:10.1029/2020ms002203.


Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers. ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, July 2017. URL:, doi:10.1109/cvpr.2017.369.


Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding neural networks through deep visualization. In Deep Learning Workshop, International Conference on Machine Learning (ICML). 2015.