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Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Challenges to the Reproducibility of Machine Learning Models in Health Care
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The Journal Impact Factor: A Brief History, Critique, and Discussion of Adverse Effects
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The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
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Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
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ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility
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Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
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Editorial: Evidence on Questionable Research Practices: The Good, the Bad, and the Ugly
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Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
inferredcites →https://doi.org/10.1016/j.ijmedinf.2022.104758openalex-snapshot
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dtoolAI: Reproducibility for Deep Learning
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Data and its (dis)contents: A survey of dataset development and use in machine learning research
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Preregistration Is Hard, And Worthwhile
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The replication crisis in psychology: An overview for theoretical and philosophical psychology.
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1,500 scientists lift the lid on reproducibility
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Could machine learning fuel a reproducibility crisis in science?
inferredcites →https://doi.org/10.1038/ijo.2015.214openalex-snapshot
The importance of prediction model validation and assessment in obesity and nutrition research
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inferredcites →https://doi.org/10.1038/s41557-021-00716-zopenalex-snapshot
Best practices in machine learning for chemistry
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inferredcites →https://doi.org/10.1038/s41592-021-01256-7openalex-snapshot
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inferredcites →https://doi.org/10.1038/s41597-022-01899-xopenalex-snapshot
MIMIC-IV, a freely accessible electronic health record dataset
inferredcites →https://doi.org/10.1038/s43586-023-00236-9openalex-snapshot
Containers for computational reproducibility
inferredcites →https://doi.org/10.1038/s43588-022-00388-wopenalex-snapshot
Seamless sharing and peer review of code
inferredcites →https://doi.org/10.1080/08874417.2023.2261010openalex-snapshot
The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions
inferredcites →https://doi.org/10.1093/ehjdh/ztac016openalex-snapshot
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inferredcites →https://doi.org/10.1093/jamia/ocx079openalex-snapshot
Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
inferredcites →https://doi.org/10.1093/reseval/rvv038openalex-snapshot
Evaluation practices and effects of indicator use—a literature review
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The natural selection of bad science
inferredcites →https://doi.org/10.1098/rsos.180448openalex-snapshot
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inferredcites →https://doi.org/10.1098/rsos.190806openalex-snapshot
An empirical assessment of transparency and reproducibility-related research practices in the social sciences (2014–2017)
inferredcites →https://doi.org/10.1098/rsta.2020.0210openalex-snapshot
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inferredcites →https://doi.org/10.1109/access.2020.3039833openalex-snapshot
Challenges for the Repeatability of Deep Learning Models
inferredcites →https://doi.org/10.1109/aipr52630.2021.9762211openalex-snapshot
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A Critical Overview of Privacy in Machine Learning
inferredcites →https://doi.org/10.1109/sii46433.2020.9025951openalex-snapshot
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning
inferredcites →https://doi.org/10.1109/transai54797.2022.00008openalex-snapshot
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inferredcites →https://doi.org/10.1111/j.1467-954x.2012.02077.xopenalex-snapshot
Living with the H-Index? Metric Assemblages in the Contemporary Academy
inferredcites →https://doi.org/10.1111/j.1740-9713.2015.00827.xopenalex-snapshot
The Reproducibility Crisis in Science: A Statistical Counterattack
inferredcites →https://doi.org/10.1126/science.359.6377.725openalex-snapshot
Artificial intelligence faces reproducibility crisis
inferredcites →https://doi.org/10.1126/science.aac4716openalex-snapshot
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inferredcites →https://doi.org/10.1126/science.aat3298openalex-snapshot
Missing data hinder replication of artificial intelligence studies
inferredcites →https://doi.org/10.1126/scitranslmed.aaf5027openalex-snapshot
What does research reproducibility mean?
inferredcites →https://doi.org/10.1126/scitranslmed.abb1655openalex-snapshot
Reproducibility in machine learning for health research: Still a ways to go
inferredcites →https://doi.org/10.1145/2723872.2723882openalex-snapshot
An introduction to Docker for reproducible research
inferredcites →https://doi.org/10.1145/3287560.3287596openalex-snapshot
Model Cards for Model Reporting
inferredcites →https://doi.org/10.1145/3457607openalex-snapshot
A Survey on Bias and Fairness in Machine Learning
inferredcites →https://doi.org/10.1145/3510003.3510163openalex-snapshot
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inferredcites →https://doi.org/10.1145/3526062.3536353openalex-snapshot
Managing Randomness to Enable Reproducible Machine Learning
inferredcites →https://doi.org/10.1145/3531146.3533231openalex-snapshot
Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI
inferredcites →https://doi.org/10.1145/3571884.3604316openalex-snapshot
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inferredcites →https://doi.org/10.1145/3582302.3582306openalex-snapshot
Management of Machine Learning Lifecycle Artifacts
inferredcites →https://doi.org/10.1145/3589806.3600044openalex-snapshot
On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness
inferredcites →https://doi.org/10.1146/annurev-psych-020821-114157openalex-snapshot
Replicability, Robustness, and Reproducibility in Psychological Science
inferredcites →https://doi.org/10.1148/rg.230180openalex-snapshot
National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence
inferredcites →https://doi.org/10.1148/ryai.2020200029openalex-snapshot
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers
inferredcites →https://doi.org/10.1148/ryai.210290openalex-snapshot
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inferredcites →https://doi.org/10.1161/circulationaha.114.014508openalex-snapshot
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)
inferredcites →https://doi.org/10.1162/qss_a_00224openalex-snapshot
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inferredcites →https://doi.org/10.1162/tacl_a_00074openalex-snapshot
Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets
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An Evaluation of the Software System Dependency of a Global Atmospheric Model
inferredcites →https://doi.org/10.1371/journal.pbio.1002165openalex-snapshot
The Economics of Reproducibility in Preclinical Research
inferredcites →https://doi.org/10.1371/journal.pbio.1002333openalex-snapshot
Reproducible Research Practices and Transparency across the Biomedical Literature
inferredcites →https://doi.org/10.1371/journal.pmed.0020124openalex-snapshot
Why Most Published Research Findings Are False
inferredcites →https://doi.org/10.1371/journal.pone.0183591openalex-snapshot
A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review
inferredcites →https://doi.org/10.15626/mp.2018.892openalex-snapshot
Computational Reproducibility via Containers in Psychology
inferredcites →https://doi.org/10.1609/aaai.v32i1.11503openalex-snapshot
State of the Art: Reproducibility in Artificial Intelligence
inferredcites →https://doi.org/10.1609/aimag.v42i3.18145openalex-snapshot
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inferredcites →https://doi.org/10.18665/sr.22353openalex-snapshot
Stanford Encyclopedia of Philosophy 2009
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Quantifying Inherent Randomness in Machine Learning Algorithms
inferredcites →https://doi.org/10.24251/hicss.2024.081openalex-snapshot
Black Box or Open Science? Assessing Reproducibility-Related Documentation in AI Research
inferredcites →https://doi.org/10.24251/hicss.2025.899openalex-snapshot
Automated Machine Learning in Research – A Literature Review
inferredcites →https://doi.org/10.3233/apc200105openalex-snapshot
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inferredcites →https://doi.org/10.3390/make4040045openalex-snapshot
How Do Deep-Learning Framework Versions Affect the Reproducibility of Neural Network Models?
inferredcites →https://doi.org/10.48550/arxiv.1806.08295openalex-snapshot
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
inferredcites →https://doi.org/10.48550/arxiv.1809.05676openalex-snapshot
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1903.03176openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.2003.12206openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.2005.00687openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.2202.11452openalex-snapshot
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“We Share Our Code Online”: Why This Is Not Enough to Ensure Reproducibility and Progress in Recommender Systems Research
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Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study
inferred← citeshttps://doi.org/10.1145/3708319.3727563openalex-snapshot
Hybrid AI for Human-Centric Personalization (HyPer)
inferred← citeshttps://doi.org/10.1145/3727582.3728683openalex-snapshot
A Qualitative Investigation into LLM-Generated Multilingual Code Comments and Automatic Evaluation Metrics
inferred← citeshttps://doi.org/10.1145/3772363.3798590openalex-snapshot
Reproducibility Beyond Artifacts: Interactional Support for Collaborative Machine Learning
inferred← citeshttps://doi.org/10.1145/3795792openalex-snapshot
Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch
inferred← citeshttps://doi.org/10.1145/3800587openalex-snapshot
Improving Methodological Standards in Recommender Systems Offline Evaluation
inferred← citeshttps://doi.org/10.1145/3805689.3812240openalex-snapshot
Unsteady Metrics and Benchmarking Cultures of AI Model Builders
inferred← citeshttps://doi.org/10.1145/3828672openalex-snapshot
Proxy-Based Evaluation and the Limits of Assurance in AI Governance
inferred← citeshttps://doi.org/10.1177/20552076251408534openalex-snapshot
Benchmarking survival machine learning models for 10-year cardiovascular disease risk prediction using large-scale electronic health records
inferred← citeshttps://doi.org/10.1186/s40163-026-00272-2openalex-snapshot
Computational text analysis on unstructured police data: a scoping review
inferred← citeshttps://doi.org/10.1515/opar-2025-0091openalex-snapshot
Archival Paradata and Artificial Intelligence in Archaeology
inferred← citeshttps://doi.org/10.21105/joss.09261openalex-snapshot
ReciPies: A Lightweight Data Transformation Pipeline for Reproducible ML
inferred← citeshttps://doi.org/10.2139/ssrn.5132480openalex-snapshot
Understanding the Development, Performance, Fairness, and Transparency of Machine Learning Models Used in Child Protection Prediction: A Systematic Review
inferred← citeshttps://doi.org/10.2139/ssrn.7459657openalex-snapshot
Misranked by Small Samples: Physics-Consistent Neural Operator Learning for Transient Gear-Tooth Contact Stress Fields
inferred← citeshttps://doi.org/10.25287/ohuiibf.1924712openalex-snapshot
Denetimde Makine Öğrenmesine Dayalı Bir Uygulama: İzolasyon Ormanı İle Anomali Analizi
inferred← citeshttps://doi.org/10.29059/cienciauat.v20i2.2042openalex-snapshot
Algoritmo estadístico para el aprendizaje en redes neuronales para la traducción hñähñu-español
inferred← citeshttps://doi.org/10.32996/jcsts.2025.7.3.53openalex-snapshot
Demystifying Data Pipelines: A Beginner's Guide to ML Data Infrastructure
inferred← citeshttps://doi.org/10.3389/fbinf.2026.1925740openalex-snapshot
Architectural good practices for reproducible benchmarking in protein machine learning
inferred← citeshttps://doi.org/10.3389/fdata.2025.1632766openalex-snapshot
Multistakeholder fairness in tourism: what can algorithms learn from tourism management?
inferred← citeshttps://doi.org/10.3389/fgene.2025.1694070openalex-snapshot
A systematic review on the generative AI applications in human medical genetics
inferred← citeshttps://doi.org/10.3389/fphy.2025.1711356openalex-snapshot
Scientific software development in the AI era: reproducibility, MLOps, and applications in soft matter physics
inferred← citeshttps://doi.org/10.3390/fi17090412openalex-snapshot
GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries
inferred← citeshttps://doi.org/10.3390/jimaging11100344openalex-snapshot
No Reproducibility, No Progress: Rethinking CT Benchmarking
inferred← citeshttps://doi.org/10.3758/s13428-026-03046-5openalex-snapshot
Measuring complex constructs in large-scale text with computational social mixed methods
inferred← citeshttps://doi.org/10.48084/etasr.19588openalex-snapshot
Robust Probabilistic Load Forecasting for Multi-Energy Buildings Using Issue-Aware and Conformally Calibrated Gradient Boosting
inferred← citeshttps://doi.org/10.54392/irjmt26518openalex-snapshot
Comparative Evaluation of Tree-Based Algorithms for Postpartum Mental Health Risk: a Methodological Benchmark
inferred← citeshttps://doi.org/10.64898/2026.01.05.697809openalex-snapshot
Can AI Conduct Autonomous Scientific Research? Case Studies on Two Real-World Tasks
inferred← citeshttps://doi.org/10.68159/g485340863openalex-snapshot
Redefining "Reproducibility" in Data-Driven Materials Synthesis: Between Algorithmic and Experimental Replication
inferred← citeshttps://doi.org/10.68159/m083609483openalex-snapshot
Perspective: Why Materials AI Needs More Failure Reporting — A Position on Negative Results as Community Infrastructure
inferred← citeshttps://doi.org/10.70577/re4ajb26openalex-snapshot
Reconfiguración de los saberes en la investigación científica actual
inferred← citeshttps://doi.org/10.7717/peerj-cs.3873openalex-snapshot
A hybrid AI factor framework for longitudinal analysis of multidimensional poverty status
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