Reproducible research policies and software/data management in scientific computing journals: a survey, discussion, and perspectives

被引:0
作者
Hernandez, Jose Armando [1 ]
Colom, Miguel [1 ]
机构
[1] Univ Paris, Ctr Borelli, CNRS, INSERM,SSA,ENS Paris Saclay, Gif Sur Yvette, France
来源
FRONTIERS IN COMPUTER SCIENCE | 2025年 / 6卷
关键词
repeatability; reproducibility; replicability; reusability; data science ML/AI; RaaS; scientific journal; trustworthy research; ARTIFICIAL-INTELLIGENCE; FUTURE; WORKFLOWS; SCIENCE; SYSTEM;
D O I
10.3389/fcomp.2024.1491823
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Introduction The recognized credibility crisis in scientific research has led to an increasing focus on reproducibility studies, particularly in computer science. Existing studies predominantly examine specific technological aspects of reproducibility but neglect the critical interplay between authors and publishers in enabling reproducible computational scientific research.Methods A systematic review was conducted following the PRISMA Literature Review methodology, complemented by a Journals Survey. This approach enabled a comprehensive analysis of reproducibility policies and software/data management practices in scientific computing journals.Results The survey revealed significant variability in reproducibility policies and practices across computer science journals. Many gaps and challenges were identified, including inconsistencies in policy enforcement, lack of standardized tools, and insufficient recognition of software as a research artifact. The analysis highlighted the potential of Reproducibility as a Service (RaaS) as an innovative solution to address these challenges.Discussion This study underscores the need for improved standardization and implementation of reproducibility policies. Strategies to enhance reproducibility include fostering collaboration among authors, publishers, and technology providers, as well as recognizing software as a critical research output. The findings aim to guide stakeholders in bridging the current gaps and advancing the reproducibility of computational scientific articles.
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页数:25
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