为数据驱动社会科学研究构建的数据湖
A Datalake for Data-driven Social Science Research
AI总结：
本文提出一个专为社会科学研究设计的数据湖平台，通过整合多种数据类型和提供可视化工具，简化研究流程并提升透明度，旨在普及高级数据科学实践。
摘要：
社会科学研究日益要求数据驱动的洞察，但研究者常常面临技术能力不足、数据格式不一致以及获取可靠数据集受限等障碍。本文提出了一种专为跨学科社会科学研究设计的数据湖基础设施。我们的系统支持多种数据类型的摄入与整合，自动记录溯源和版本追踪，支持基于角色的访问控制，并内置可视化和分析工具。我们通过涵盖治理、健康和教育等领域的现实应用场景展示了数据湖的实用性。对其中一个应用场景——分析收入、教育与婴儿死亡率之间的关系——的详细演示展示了我们的平台如何简化研究流程，同时保持透明性和可重复性。我们认为此类基础设施可以普及高级数据科学实践，特别是对非政府组织、学生和基层组织而言。数据湖将持续发展，计划支持机器学习流水线、移动访问和公民数据反馈机制。
英文摘要：
Social science research increasingly demands data-driven insights, yet researchers often face barriers such as lack of technical expertise, inconsistent data formats, and limited access to reliable datasets.Social science research increasingly demands data-driven insights, yet researchers often face barriers such as lack of technical expertise, inconsistent data formats, and limited access to reliable datasets. In this paper, we present a Datalake infrastructure tailored to the needs of interdisciplinary social science research. Our system supports ingestion and integration of diverse data types, automatic provenance and version tracking, role-based access control, and built-in tools for visualization and analysis. We demonstrate the utility of our Datalake using real-world use cases spanning governance, health, and education. A detailed walkthrough of one such use case -- analyzing the relationship between income, education, and infant mortality -- shows how our platform streamlines the research process while maintaining transparency and reproducibility. We argue that such infrastructure can democratize access to advanced data science practices, especially for NGOs, students, and grassroots organizations. The Datalake continues to evolve with plans to support ML pipelines, mobile access, and citizen data feedback mechanisms.
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