[目的/意义]探究科研大数据生态治理的影响因素，有利于促进科研大数据的良性发展和数据资源的优 化再生。［方法/过程］文章运用扎根理论的研究方法，分析了不同领域人员对于科研大数据的使用需求，构建了原 生、伴生、次生影响因素框架模型，并对其影响因素之间进行了关联性分析，最后针对不同群体的使用需求提出了 数据治理的优化建议。［结果/结论］科研大数据生态治理受原生数据、伴生环境、次生资源等因素影响，具体来说， 数据初始质量和传播过程影响着原生数据质量，同时受数据治理架构规范性、治理技术适配度、人才积聚量等伴 生因素的影响，最后输出的数据资源则作为次生因素对治理过程进行着反馈与更新，总体上形成了“输入+输出” “内调节+外循环”“治理+反馈”的生态化治理框架。

[Purpose/significance] Exploring the influencing factors of ecological governance of scientific research big data is conducive to promoting the benign development of scientific research big data and the optimal regeneration of data resources. [Method/process] Based on the research method of grounded theory, this thesis conducted interviews with the survey subjects and summarized the interview data. Based on this, it analyzed the needs of people in different fields for the use of big data in scientific research, built the framework model of primary, associated and secondary influ? encing factors, and analyzed the correlation among the influencing factors. Finally, the paper puts forward some optimi? zation suggestions for data governance according to the needs of different groups. [Result/conclusion] The ecological governance of scientific research big data is affected by the original data, associated environment, secondary resources and other factors. Specifically, the initial quality and transmission process of data affect the quality of the original data, and are also affected by associated factors such as the standardization of data governance architecture, the fit degree of governance technology, and the amount of talent accumulation. The final output data resources are used as secondary factors to feedback and update the governance process. On the whole, an ecological governance framework of "input + output", "internal regulation + external circulation" and "governance + feedback" has been formed.