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Scientific progress in NLP rests on the reproducibility of researchers' claims.
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily B. Fox, and H. Larochelle. 2021 · 2003
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Scikit-learn: Machine learning in Python
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. 2011 · 2011
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Offspring from reproduction problems: What replication failure teaches us
Antske Fokkens, Marieke van Erp, Marten Postma, Ted Pedersen, Piek Vossen, and Nuno Freire. 2013 · 2013
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Practices in source code sharing in astrophysics
Lior Shamir, John F Wallin, Alice Allen, Bruce Berriman, Peter Teuben, Robert J Nemiroff, Jessica Mink, Robert J Hanisch, and Kimberly DuPrie. 2013 · 2013
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Estimating the reproducibility of psychological science
Alexander A. Aarts, Joanna E. Anderson, Christopher J. Anderson, and et al. 2015 · 2015
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1,500 scientists lift the lid on reproducibility
Monya Baker. 2016 · 2016
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Toward standard practices for sharing computer code and programs in neuroscience
Stephen J Eglen, Ben Marwick, Yaroslav O Halchenko, Michael Hanke, Shoaib Sufi, Padraig Gleeson, R Angus Silver, Andrew P Davison, Linda Lanyon, Mathew Abrams, et al. 2017 · 2017
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M. Bender and Batya Friedman. 2018 · 2018
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Datasheets for datasets
Timnit Gebru, Jamie H. Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé, and Kate Crawford. 2018 · 2018
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State of the art: Reproducibility in artificial intelligence
Odd Erik Gundersen and Sigbjørn Kjensmo. 2018 · 2018
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Checklists work to improve science
Nature. 2018 · 2018
Cited alongside, same era.
The #benderrule: On naming the languages we study and why it matters
Emily M. Bender. 2019 · 2019
Cited alongside, same era.
The plos one collection on machine learning in health and biomedicine: Towards open code and open data
Leo A Celi, Luca Citi, Marzyeh Ghassemi, and Tom J Pollard. 2019 · 2019
Cited alongside, same era.
The icml 2019 code-at-submit-time experiment
Kamalika Chaudhuri and Ruslan Salakhutdinov. 2019 · 2019
Cited alongside, same era.
Show your work: Improved reporting of experimental results
Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
Reproducibility in machine learning for health
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, and Luca Foschini. 2019 · 2019
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. 2020 · 2020
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021 · 2021
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Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
Ben Hutchinson, Andrew Smart, A. Hanna, Emily L. Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, and Margaret Mitchell. 2020 · 2021
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Scientific credibility of machine translation research: A meta-evaluation of 769 papers
Benjamin Marie, Atsushi Fujita, and Raphael Rubino. 2021 · 2021
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Cited alongside, same era.
Do ImageNet classifiers generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019 · 2019
Cited alongside, same era.
Guest post: Reproducibility at EMNLP 2020
Jesse Dodge and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
Transparency and reproducibility in artificial intelligence
Benjamin Haibe-Kains, George Adam, Ahmed Hosny, Farnoosh Khodakarami, Levi Waldron, Bo Wang, Chris McIntosh, Anna Goldenberg, Anshul Kundaje, Casey S. Greene, Tamara Broderick, Michael M. Hoffman, Jeffrey T. Leek, Keegan D. Korthauer, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor J. Hastie, John P. A. Ioannidis, John Quackenbush, and Hugo J.W.L. Aerts. 2020 · 2020
Cited alongside, same era.
Changing the world by changing the data
Anna Rogers. 2021 · 2021
Later among the works it cites.
‘just what do you think you’re doing, dave?’ a checklist for responsible data use in NLP
Anna Rogers, Timothy Baldwin, and Kobi Leins. 2021 · 2021
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Association between author metadata and acceptance: A feature-rich, matched observational study of a corpus of iclr submissions between 2017-2022
Chang Chen, Jiayao Zhang, Dan Roth, Ting Ye, and Bo Zhang. 2022 · 2022
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What do nlp researchers believe? results of the nlp community metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, and Samuel R. Bowman. 2022 · 2022
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Data cards: Purposeful and transparent dataset documentation for responsible ai
Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. 2022 · 2022
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