van Kampen Antoine H C, Mahamune Utkarsh, Jongejan Aldo, van Schaik Barbera D C, Balashova Daria, Lashgari Danial, Pras-Raves Mia, Wever Eric J M, Dane Adrie D, García-Valiente Rodrigo, Moerland Perry D
Amsterdam UMC, University of Amsterdam, Bioinformatics Laboratory, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands.
Netherlands. Amsterdam Public Health, Methodology, Amsterdam, Netherlands.
Nat Commun. 2024 Sep 16;15(1):8117. doi: 10.1038/s41467-024-52446-8.
Reproducibility of computational research is often challenging despite established guidelines and best practices. Translating these guidelines into practical applications remains difficult. Here, we present ENCORE, an approach to enhance transparency and reproducibility by guiding researchers in how to structure and document a computational project. ENCORE builds on previous efforts in computational reproducibility and integrates all project components into a standardized file system structure. It utilizes pre-defined files as documentation templates, leverages GitHub for software versioning, and includes an HTML-based navigator. ENCORE is designed to be agnostic to the type of computational project, data, programming language, and ICT infrastructure, and does not rely on specific software tools. We also share our group's experience using ENCORE, highlighting that the most significant challenge to the routine adoption of approaches like ours is the lack of incentives to motivate researchers to dedicate sufficient time and effort to ensure reproducibility.
尽管有既定的指南和最佳实践,但计算研究的可重复性往往具有挑战性。将这些指南转化为实际应用仍然很困难。在这里,我们提出了ENCORE,这是一种通过指导研究人员如何构建和记录计算项目来提高透明度和可重复性的方法。ENCORE建立在先前计算可重复性的努力基础上,并将所有项目组件集成到标准化的文件系统结构中。它利用预定义文件作为文档模板,利用GitHub进行软件版本控制,并包括一个基于HTML的导航器。ENCORE旨在对计算项目的类型、数据、编程语言和信息通信技术基础设施不做区分,并且不依赖于特定的软件工具。我们还分享了我们团队使用ENCORE的经验,强调像我们这样的方法在常规采用中面临的最重大挑战是缺乏激励措施来促使研究人员投入足够的时间和精力来确保可重复性。