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Interact."}, {"key": "10.1016/j.patter.2023.100804_bib69", "doi-asserted-by": "crossref", "first-page": "86", "DOI": "10.1145/3458723", "article-title": "Datasheets for datasets", "volume": "64", "author": "Gebru", "year": "2021", "journal-title": "Commun. ACM"}, {"key": "10.1016/j.patter.2023.100804_bib70", "doi-asserted-by": "crossref", "DOI": "10.1371/journal.pone.0183591", "article-title": "A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review", "volume": "12", "author": "Han", "year": "2017", "journal-title": "PLoS One"}, {"key": "10.1016/j.patter.2023.100804_bib71", "doi-asserted-by": "crossref", "DOI": "10.1148/ryai.210127", "article-title": "Assessing Methods and Tools to Improve Reporting, Increase Transparency, and Reduce Failures in Machine Learning Applications in Health Care", "volume": "4", "author": "Garbin", "year": "2022", "journal-title": "Radiol. Artif. Intell."}, {"key": "10.1016/j.patter.2023.100804_bib72", "first-page": "1", "article-title": "AI and the Everything in the Whole Wide World Benchmark", "author": "Raji", "year": "2021", "journal-title": "Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks"}, {"key": "10.1016/j.patter.2023.100804_bib73", "doi-asserted-by": "crossref", "first-page": "532", "DOI": "10.1177/00031224211004187", "article-title": "What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory", "volume": "86", "author": "Lundberg", "year": "2021", "journal-title": "Am. Socio. Rev."}, {"key": "10.1016/j.patter.2023.100804_bib74", "doi-asserted-by": "crossref", "DOI": "10.1177/2378023119849803", "article-title": "Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge", "volume": "5", "author": "Liu", "year": "2019", "journal-title": "Socius."}, {"key": "10.1016/j.patter.2023.100804_bib75", "doi-asserted-by": "crossref", "first-page": "87", "DOI": "10.1093/pan/mpv024", "article-title": "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced CivilWar Onset Data", "volume": "24", "author": "Muchlinski", "year": "2016", "journal-title": "Polit. Anal."}, {"key": "10.1016/j.patter.2023.100804_bib76", "first-page": "193", "article-title": "Do the robot: Lessons from machine learning to improve conflict forecasting", "volume": "54", "author": "Colaresi", "year": "2017", "journal-title": "J. Peace Res."}, {"key": "10.1016/j.patter.2023.100804_bib77", "doi-asserted-by": "crossref", "unstructured": "Wang, Y. Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment. Political Analysis 2019, 27, Publisher: Cambridge University Press, 107–110.", "DOI": "10.1017/pan.2018.40"}, {"key": "10.1016/j.patter.2023.100804_bib78", "series-title": "Improving Supreme Court Forecasting Using Boosted Decision Trees. Political Analysis", "first-page": "381", "volume": "27", "author": "Kaufman", "year": "2019"}, {"key": "10.1016/j.patter.2023.100804_bib79", "series-title": "Forecasting civil war and political violence", "first-page": "177", "author": "Bara", "year": "2020"}, {"key": "10.1016/j.patter.2023.100804_bib80", "series-title": "Scimago Journal and Country Rank", "year": "2020"}, {"key": "10.1016/j.patter.2023.100804_bib81", "first-page": "363", "article-title": "The perils of policy by p-value: Predicting civil conflicts", "volume": "47", "author": "Ward", "year": "2010", "journal-title": "J. Peace Res."}, {"key": "10.1016/j.patter.2023.100804_bib82", "doi-asserted-by": "crossref", "first-page": "199", "DOI": "10.1214/ss/1009213726", "article-title": "Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)", "volume": "16", "author": "Breiman", "year": "2001", "journal-title": "Stat. Sci."}, {"key": "10.1016/j.patter.2023.100804_bib83", "doi-asserted-by": "crossref", "first-page": "eaao5580", "DOI": "10.1126/sciadv.aao5580", "article-title": "The accuracy, fairness, and limits of predicting recidivism", "volume": "4", "author": "Dressel", "year": "2018", "journal-title": "Sci. Adv."}, {"key": "10.1016/j.patter.2023.100804_bib84", "first-page": "192", "article-title": "Data-driven advice for applying machine learning to bioinformatics problems. Pacific Symposium on Biocomputing", "volume": "23", "author": "Olson", "year": "2018", "journal-title": "Pac. Symp. Biocomput."}, {"key": "10.1016/j.patter.2023.100804_bib85", "series-title": "In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", "first-page": "2786", "article-title": "We Need to Talk about Standard Splits", "author": "Gorman", "year": "2019"}, {"key": "10.1016/j.patter.2023.100804_bib86", "doi-asserted-by": "crossref", "first-page": "1885", "DOI": "10.1177/0022002720918923", "article-title": "Forecasting Civil Wars: Theory and Structure in an Age of “Big Data” and Machine Learning", "volume": "64", "author": "Blair", "year": "2020", "journal-title": "J. Conflict Resolut."}, {"key": "10.1016/j.patter.2023.100804_bib87", "doi-asserted-by": "crossref", "first-page": "77", "DOI": "10.1186/1471-2105-12-77", "article-title": "pROC: an open-source package for R and S+ to analyze and compare ROC curves", "volume": "12", "author": "Robin", "year": "2011", "journal-title": "BMC Bioinf."}, {"key": "10.1016/j.patter.2023.100804_bib88", "article-title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification", "author": "He", "year": "2015", "journal-title": "CoRR"}, {"key": "10.1016/j.patter.2023.100804_bib89", "series-title": "Computer Vision: Algorithms and Applications", "author": "Szeliski", "year": "2021"}, {"key": "10.1016/j.patter.2023.100804_bib90", "doi-asserted-by": "crossref", "DOI": "10.1097/MD.0000000000015987", "article-title": "The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses", "volume": "98", "author": "Shi", "year": "2019", "journal-title": "Medicine"}, {"key": "10.1016/j.patter.2023.100804_bib91", "doi-asserted-by": "crossref", "first-page": "175", "DOI": "10.1038/nature25753", "article-title": "Meta-analysis and the science of research synthesis", "volume": "555", "author": "Gurevitch", "year": "2018", "journal-title": "Nature"}, {"key": "10.1016/j.patter.2023.100804_bib92", "doi-asserted-by": "crossref", "first-page": "486", "DOI": "10.1126/science.aal3856", "article-title": "Prediction and explanation in social systems", "volume": "355", "author": "Hofman", "year": "2017", "journal-title": "Science"}, {"key": "10.1016/j.patter.2023.100804_bib93", "series-title": "Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control", "author": "Islam", "year": "2017"}, {"key": "10.1016/j.patter.2023.100804_bib94", "unstructured": "Lones, M. A. How to avoid machine learning pitfalls: a guide for academic researchers.Preprint at arXiv:2108.02497 [cs] 2021,https://doi.org/10.48550/arXiv.2108.02497 arXiv: 2108.02497."}, {"key": "10.1016/j.patter.2023.100804_bib95", "doi-asserted-by": "crossref", "first-page": "211", "DOI": "10.1007/s11263-015-0816-y", "article-title": "ImageNet Large Scale Visual Recognition Challenge", "volume": "115", "author": "Russakovsky", "year": "2015", "journal-title": "Int. J. Comput. Vis."}, {"key": "10.1016/j.patter.2023.100804_bib96", "series-title": "In Proceedings of the 38th International Conference on Machine Learning", "first-page": "5637", "author": "Koh", "year": "2021"}, {"key": "10.1016/j.patter.2023.100804_bib97", "doi-asserted-by": "crossref", "DOI": "10.1177/25152459211026864", "article-title": "Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction", "volume": "4", "author": "Rocca", "year": "2021", "journal-title": "Advances in Methods and Practices in Psychological Science"}, {"key": "10.1016/j.patter.2023.100804_bib98", "doi-asserted-by": "crossref", "first-page": "745", "DOI": "10.1080/10618600.2017.1384734", "article-title": "50 Years of Data Science", "volume": "26", "author": "Donoho", "year": "2017", "journal-title": "J. Comput. Graph Stat."}, {"key": "10.1016/j.patter.2023.100804_bib99", "series-title": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing", "first-page": "7297", "article-title": "Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers", "author": "Marie", "year": "2021"}, {"key": "10.1016/j.patter.2023.100804_bib100", "doi-asserted-by": "crossref", "DOI": "10.15626/MP.2018.892", "article-title": "Computational reproducibility via containers in social psychology", "volume": "3", "author": "Clyburne-Sherin", "year": "2019", "journal-title": "Meta-Psychology"}, {"key": "10.1016/j.patter.2023.100804_bib101", "doi-asserted-by": "crossref", "first-page": "641", "DOI": "10.1038/s41592-018-0137-5", "article-title": "Easing the burden of code review", "volume": "15", "year": "2018", "journal-title": "Nat. Methods"}, {"key": "10.1016/j.patter.2023.100804_bib102", "doi-asserted-by": "crossref", "DOI": "10.1126/science.aay9534", "article-title": "No coding required: Companies make it easier than ever for scientists to use artificial intelligence", "author": "Hutson", "year": "2019", "journal-title": "Science"}, {"key": "10.1016/j.patter.2023.100804_bib103", "series-title": "Claims of Superior Performance of Machine Learning over Logistic Regression for Civil War Prediction Don’t Reproduce", "author": "Kapoor", "year": "2021"}, {"key": "10.1016/j.patter.2023.100804_bib104", "doi-asserted-by": "crossref", "DOI": "10.3389/frma.2018.00023", "article-title": "Dimensions: Building Context for Search and Evaluation", "volume": "3", "author": "Hook", "year": "2018", "journal-title": "Frontiers in Research Metrics and Analytics"}], "container-title": ["Patterns"], "original-title": [], "language": "en", "link": [{"URL": "https://api.elsevier.com/content/article/PII:S2666389923001599?httpAccept=text/xml", "content-type": "text/xml", "content-version": "vor", "intended-application": "text-mining"}, {"URL": "https://api.elsevier.com/content/article/PII:S2666389923001599?httpAccept=text/plain", "content-type": "text/plain", "content-version": "vor", "intended-application": "text-mining"}], "deposited": {"date-parts": [[2025, 11, 12]], "date-time": "2025-11-12T23:50:33Z", "timestamp": 1762991433000}, "score": 1, "resource": {"primary": {"URL": "https://linkinghub.elsevier.com/retrieve/pii/S2666389923001599"}}, "subtitle": [], "short-title": [], "issued": {"date-parts": [[2023, 9]]}, "references-count": 104, "journal-issue": {"issue": "9", "published-print": {"date-parts": [[2023, 9]]}}, "alternative-id": ["S2666389923001599"], "URL": "https://doi.org/10.1016/j.patter.2023.100804", "relation": {}, "ISSN": ["2666-3899"], "issn-type": [{"value": "2666-3899", "type": "print"}], "subject": [], "published": {"date-parts": [[2023, 9]]}, "assertion": [{"value": "Elsevier", "name": "publisher", "label": "This article is maintained by"}, {"value": "Leakage and the reproducibility crisis in machine-learning-based science", "name": "articletitle", "label": "Article Title"}, {"value": "Patterns", "name": "journaltitle", "label": "Journal Title"}, {"value": "https://doi.org/10.1016/j.patter.2023.100804", "name": "articlelink", "label": "CrossRef DOI link to publisher maintained version"}, {"value": "article", "name": "content_type", "label": "Content Type"}, {"value": "© 2023 The Author(s).", "name": "copyright", "label": "Copyright"}], "article-number": "100804"}}