Reproducibility in research remains hindered by complex systems involving data, models, tools, and algorithms. Studies highlight a reproducibility crisis due to a lack of standardized reporting, code and data sharing, and rigorous evaluation. This paper introduces the concept of Continuous Analysis to address the reproducibility challenges in scientific research, extending the DevOps lifecycle. Continuous Analysis proposes solutions through version control, analysis orchestration, and feedback mechanisms, enhancing the reliability of scientific results. By adopting CA, the scientific community can ensure the validity and generalizability of research outcomes, fostering transparency and collaboration and ultimately advancing the field.

翻译：研究可复现性仍受涉及数据、模型、工具和算法的复杂系统所阻碍。研究指出，由于缺乏标准化报告、代码与数据共享以及严格评估，已出现可复现性危机。本文引入持续分析概念以应对科学研究中的可复现性挑战，延伸了DevOps生命周期。持续分析通过版本控制、分析编排与反馈机制提出解决方案，从而提升科学结果的可靠性。采用持续分析可使科学界确保研究结果的有效性与普适性，促进透明度与合作，最终推动领域发展。