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Reproducibility in NLP: What Have We Learned from the Checklist?
13 references·0 citing·51 second hop
Reproducibility in NLP: What Have We Learned from the Checklist? (2023)Reproducibility in NLP: What Have We Lear…Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI (2022)Data Cards: Purposeful and Tr…Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2021)Towards Accountability for Ma…‘Just What do You Think You’re Doing, Dave?’ A Checklist for Responsible Data Use in NLP (2021)‘Just What do You Think You’r…Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) (2020)Improving Reproducibility in …Show Your Work: Improved Reporting of Experimental Results (2019)Show Your Work: Improved Repo…Reproducibility in Machine Learning for Health (2019)Reproducibility in Machine Le…State of the Art: Reproducibility in Artificial Intelligence (2018)State of the Art: Reproducibi…Checklists work to improve science (2018)Checklists work to improve sc…Toward standard practices for sharing computer code and programs in neuroscience (2017)Toward standard practices for…1,500 scientists lift the lid on reproducibility (2016)1,500 scientists lift the lid…Estimating the reproducibility of psychological science (2015)Estimating the reproducibilit…Practices in source code sharing in astrophysics (2013)Practices in source code shar…Offspring from Reproduction Problems: What Replication Failure Teaches Us (2013)Offspring from Reproduction P…Why Most Published Research Findings Are False (2005)Conducting Meta-Analyses in R with the metafor Package (2010)Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs (2013)Estimating Psychological Networks and their Accuracy: A Tutorial Paper (2016)A tutorial on regularized partial correlation networks. (2018)The Proof and Measurement of Association between Two Things (1904)A NEW MEASURE OF RANK CORRELATION (1938)Contextual correlates of semantic similarity (1991)On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications (2018)Reproducibility in Computational Linguistics: Are We Willing to Share? (2018)A Systematic Review of Reproducibility Research in Natural Language Processing (2021)Journal of Computational and Graphical Statistics (2014)An introduction to Docker for reproducible research (2015)Promoting an open research culture (2015)Digital open science—Teaching digital tools for reproducible and transparent research (2018)Experimenting with reproducibility: a case study of robustness in bioinformatics (2018)Reproducibility in Neuroimaging Analysis: Challenges and Solutions (2022)Believe it or not: how much can we rely on published data on potential drug targets? (2011)Reproducible Research in Computational Science (2011)Redefine statistical significance (2017)Threats of a replication crisis in empirical computer science (2020)Applications of deep learning in stock market prediction: Recent progress (2021)Reproducibility in machine learning for health research: Still a ways to go (2021)The Statistical Crisis in Science (2014)Troubling Trends in Machine Learning Scholarship (2019)Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020)On the Opportunities and Risks of Foundation Models (2021)The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021)A manifesto for reproducible science (2017)A large-scale study on research code quality and execution (2022)The Fallacy of AI Functionality (2022)REFORMS: Consensus-based Recommendations for Machine-learning-based Science (2024)Dilemmas in a general theory of planning (1973)Science in action : how to follow scientists and engineers through society (1987)Science in Action: How to Follow Scientists and Engineers through Society (1988)Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021)On the genealogy of machine learning datasets: A critical history of ImageNet (2021)Experimental evidence of massive-scale emotional contagion through social networks (2014)The global landscape of AI ethics guidelines (2019)Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020)Handling and Presenting Harmful Text in NLP Research (2022)NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Keeping (2023)Data Authenticity, Consent, and Provenance for AI Are All Broken: What Will It Take to Fix Them? (2024)This is Not a Boundary Object: Reflections on the Origin of a Concept (2010)Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018)Model Cards for Model Reporting (2019)Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges (2023)Understanding accountability in algorithmic supply chains (2023)What is Human-Centered about Human-Centered AI? A Map of the Research Landscape (2023)Bioconda: sustainable and comprehensive software distribution for the life sciences (2018)The nf-core framework for community-curated bioinformatics pipelines (2020)
this work references cited by second hop

References

  • 2015Estimating the reproducibility of psychological science8,516 cited
  • 20161,500 scientists lift the lid on reproducibility4,200 cited
  • 2018State of the Art: Reproducibility in Artificial Intelligence292 cited
  • 2021Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure279 cited
  • 2019Show Your Work: Improved Reporting of Experimental Results232 cited
  • 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)228 cited
  • 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI228 cited
  • 2017Toward standard practices for sharing computer code and programs in neuroscience111 cited
  • 2013Offspring from Reproduction Problems: What Replication Failure Teaches Us105 cited
  • 2021‘Just What do You Think You’re Doing, Dave?’ A Checklist for Responsible Data Use in NLP42 cited
  • 2018Checklists work to improve science38 cited
  • 2013Practices in source code sharing in astrophysics37 cited
  • 2019Reproducibility in Machine Learning for Health34 cited

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