Abstract: Open science plays a crucial role in promoting scientific innovation and research transparency. In the context of the ongoing transformation towards a new paradigm of scientific research, knowledge growth is accompanied by a crisis of trust. Researchers are increasingly focused on the openness and transparency of experimental data and research methods to ensure the reproducibility of research results. This paper employs bibliometric analysis methods to explore the main issues and impacts of open science in terms of reproducibility, both domestically and internationally, by analyzing relevant literature from Web of Science and CNKI (China National Knowledge Infrastructure). The study finds that reproducibility issues primarily involve challenges in experimental conditions, data processing, and research culture. These problems not only hinder the effective reproduction of scientific results but also affect the credibility of scientific research. To address these challenges, open science proposes a series of solutions, including promoting data sharing, standardizing experimental methods, and improving evaluation systems. The concept of open science has emerged as a response to the growing concern over the reproducibility crisis in scientific research. This crisis has been particularly prominent in fields such as psychology, medicine, and biology, where numerous high-profile studies have failed to be replicated. The inability to reproduce research findings undermines the fundamental principles of scientific inquiry and erodes public trust in scientific institutions.One of the primary issues identified in the study is the lack of transparency in experimental conditions. Researchers often fail to provide sufficiently detailed information about their experimental setups, materials, and procedures. This omission makes it difficult for other scientists to recreate the exact conditions under which the original experiments were conducted, leading to discrepancies in results. Open science advocates for comprehensive reporting of experimental protocols, including seemingly minor details that could potentially influence outcomes.Data processing presents another significant challenge to reproducibility. The increasing complexity of scientific data, particularly in fields involving big data and advanced statistical analyses, has made it more difficult to ensure consistency in data handling and interpretation. Open science initiatives promote the use of standardized data processing pipelines and encourage researchers to make their raw data and analysis scripts publicly available. This approach not only allows for independent verification of results but also facilitates the development of more robust analytical methods through collaborative efforts.The research culture within scientific communities also plays a crucial role in the reproducibility crisis. The pressure to publish novel and positive results has sometimes led to questionable research practices, such as p-hacking or selective reporting of data. Open science seeks to address these issues by promoting a culture of transparency and integrity. This includes encouraging the pre-registration of studies, publishing null results, and valuing replication studies as important contributions to scientific knowledge.To combat these challenges, open science proposes several solutions. First, it advocates for widespread data sharing through repositories and platforms that make research data freely accessible to the scientific community. This not only supports reproducibility efforts but also accelerates scientific progress by allowing researchers to build upon existing datasets.Second, open science promotes the standardization of experimental methods across disciplines. This includes developing and adhering to best practices for experimental design, data collection, and analysis. Standardization helps ensure that experiments conducted in different laboratories can be more easily compared and replicated.Third, open science calls for a reform of the scientific evaluation system. Traditional metrics of scientific success, such as the number of publications or journal impact factors, may inadvertently incentivize practices that compromise reproducibility. Open science proposes alternative evaluation criteria that place greater emphasis on research quality, transparency, and reproducibility rather than novelty alone.Looking to the future, open science is expected to play an increasingly important role in several key areas. It will continue to enhance research transparency by promoting open access publication models and encouraging the sharing of not only final results but also the entire research process, including negative outcomes and failed experiments. Furthermore, open science is anticipated to foster greater collaboration among researchers across institutional and national boundaries. By breaking down barriers to data access and promoting open communication, it enables scientists to pool resources, share expertise, and tackle complex problems more effectively. Finally, open science is poised to optimize the overall scientific ecosystem. This includes developing new infrastructure for data management and sharing, creating innovative platforms for peer review and scientific discourse, and establishing new norms for scientific conduct that prioritize integrity and reproducibility.In conclusion, while the challenges to reproducibility in scientific research are significant, the principles and practices of open science offer promising solutions. By promoting transparency, standardization, and collaboration, open science not only addresses the current reproducibility crisis but also lays the foundation for a more robust, efficient, and trustworthy scientific enterprise in the future.