A 5-year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the ‘team science’ approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards’s model of ‘making global data’ – collecting signals via consistent methods, technologies, and policies – to ‘make data global’ – comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.

中文翻译：

---

大规模协作定性研究：对 20 年全球数据获取和数据全球化的思考

从 1999 年开始的一个为期 5 年的地理科学数据应用研究项目，演变为对传感器网络、环境科学、生物学、地震学、海底科学、生物医学、天文学和其他领域的数据实践的 20 年研究。通过模仿所研究的科学家的“团队科学”方法，加州大学洛杉矶分校知识基础设施中心收集了关于科学家如何跨领域生成、管理、使用和重用数据的综合定性数据。基于 Paul N. Edwards 的“制作全球数据”模型——通过一致的方法、技术和政策收集信号——“使数据全球化”——比较和整合这些数据，研究团队已经管理和利用这些数据作为协作资源。这篇文章反映了社会、技术、组织、该团队在从新旧数据中创造新知识时遇到了经济和政策挑战。我们反思几代学生和教职员工的连续性、拨款之间的过渡、软件工具之间遗留数据的转移、研究方法以及专业数据管理人员在社会科学中的作用。