ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever et al.
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Durham Research Online (Durham University)
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2022
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Full list of authors: Price-Whelan, Adrian M.; Lim, Pey Lian; Earl, Nicholas; Starkman, Nathaniel; Bradley, Larry; Shupe, David L.; Patil, Aarya A.; Corrales, Lia; Brasseur, C. E.; Noethe, Maximilian; Donath, Axel; Tollerud, Erik; Morris, Brett M.; Ginsburg, Adam; Vaher, Eero; Weaver, Benjamin A.; Tocknell, James; Jamieson, William; van Kerkwijk, Marten H.; Robitaille, Thomas P.; Merry, Bruce; Bachetti, Matteo; Gunther, H. Moritz; Aldcroft, Thomas L.; Alvarado-Montes, Jaime A.; Archibald, Anne M.; Bodi, Attila; Bapat, Shreyas; Barentsen, Geert; Bazan, Juanjo; Biswas, Manish; Boquien, Mederic; Burke, D. J.; Cara, Daria; Cara, Mihai; Conroy, Kyle E.; Conseil, Simon; Craig, Matthew W.; Cross, Robert M.; Cruz, Kelle L.; D'Eugenio, Francesco; Dencheva, Nadia; Devillepoix, Hadrien A. R.; Dietrich, Jorg P.; Eigenbrot, Arthur Davis; Erben, Thomas; Ferreira, Leonardo; Foreman-Mackey, Daniel; Fox, Ryan; Freij, Nabil; Garg, Suyog; Geda, Robel; Glattly, Lauren; Gondhalekar, Yash; Gordon, Karl D.; Grant, David; Greenfield, Perry; Groener, Austen M.; Guest, Steve; Gurovich, Sebastian; Handberg, Rasmus; Hart, Akeem; Hatfield-Dodds, Zac; Homeier, Derek; Hosseinzadeh, Griffin; Jenness, Tim; Jones, Craig K.; Joseph, Prajwel; Kalmbach, J. Bryce; Karamehmetoglu, Emir; Kaluszynski, Mikolaj; Kelley, Michael S. P.; Kern, Nicholas; Kerzendorf, Wolfgang E.; Koch, Eric W.; Kulumani, Shankar; Lee, Antony; Ly, Chun; Ma, Zhiyuan; MacBride, Conor; Maljaars, Jakob M.; Muna, Demitri; Murphy, N. A.; Norman, Henrik; O'Steen, Richard; Oman, Kyle A.; Pacifici, Camilla; Pascual, Sergio; Pascual-Granado, J.; Patil, Rohit R.; Perren, Gabriel, I; Pickering, Timothy E.; Rastogi, Tanuj; Roulston, Benjamin R.; Ryan, Daniel F.; Rykoff, Eli S.; Sabater, Jose; Sakurikar, Parikshit; Salgado, Jesus; Sanghi, Aniket; Saunders, Nicholas; Savchenko, Volodymyr; Schwardt, Ludwig; Seifert-Eckert, Michael; Shih, Albert Y.; Jain, Anany Shrey; Shukla, Gyanendra; Sick, Jonathan; Simpson, Chris; Singanamalla, Sudheesh; Singer, Leo P.; Singhal, Jaladh; Sinha, Manodeep; Sipocz, Brigitta M.; Spitler, Lee R.; Stansby, David; Streicher, Ole; Sumak, Jani; Swinbank, John D.; Taranu, Dan S.; Tewary, Nikita; Tremblay, Grant R.; De Val-Borro, Miguel; Vasovic, Zlatan; Van Kooten, Samuel J.; Verma, Shresth; Cardoso, Jose Vinicius de Miranda; Williams, Peter K. G.; Wilson, Tom J.; Winkel, Benjamin; Wood-Vasey, W. M.; Xue, Rui; Yoachim, Peter; Zhang, Chen; Zonca, Andrea; Astropy Project Contributors; TARDIS Collaboration; Astropy Coordination Comm.--This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
The Astropy Project represents a critical case study in how large-scale, community-driven open-source software can be sustained and grown over time. In an era where scientific research increasingly relies on software, Astropy provides a model for collaborative development that spans institutions, career stages, and geographic boundaries. The paper's focus on the v5.0 release of the core package underscores the project's maturity and its ongoing commitment to improving the tools available to astronomers.
Beyond astronomy, Astropy's approach to governance, community engagement, and technical infrastructure offers valuable lessons for any open-source scientific project. The paper documents the strategies that have allowed Astropy to maintain a vibrant contributor community while ensuring the reliability and usability of its software. This is particularly relevant as the research community grapples with challenges of software sustainability and reproducibility.
The paper does not present quantitative benchmarks but highlights the project's growth: Astropy v5.0 incorporates contributions from hundreds of developers and is used across thousands of research projects. The release includes enhancements such as improved unit handling, faster I/O operations, and expanded cosmological calculations. The project's success is evidenced by its widespread adoption and the active participation of its community.
Astropy's significance extends beyond its technical capabilities. It has become a cornerstone of modern astronomical research, enabling reproducible science and reducing duplicated effort. The project's emphasis on community building and sustainability provides a blueprint for other scientific software initiatives. As AI and machine learning increasingly intersect with astronomy, Astropy's role as a data manipulation and analysis tool will likely grow, making its continued development and community health ever more important. The paper's documentation of best practices in open-source governance and collaboration is a valuable resource for the broader research software engineering community.
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