Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration

作者：Siqin Wang, Xiaoxiao Huang, Mengxi Zhang, Shuming Bao, Lingbo Liu, Xiaokang Fu, Ting Zhang, Yongze Song, Peter Kedron, John P. Wilson, Xinyue Ye, Chaowei Yang, Weihe Wendy Guan · 发表于：Computational Urban Science · 年份：2025 · DOI：10.1007/s43762-025-00165-1 · 被引用次数：8 · 研究领域：Research Data Management Practices、Scientific Computing and Data Management、Big Data Technologies and Applications

Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise.