Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration
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 researc...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
2025
|
| Online Access: | http://hdl.handle.net/20.500.11937/97967 |
| _version_ | 1848766345614721024 |
|---|---|
| author | Wang, S. Huang, X. Zhang, M. Bao, S. Liu, L. Fu, X. Zhang, T. Song, Yongze Kedron, P. Wilson, J., Ye, X. Yang, C. Guan, W. |
| author_facet | Wang, S. Huang, X. Zhang, M. Bao, S. Liu, L. Fu, X. Zhang, T. Song, Yongze Kedron, P. Wilson, J., Ye, X. Yang, C. Guan, W. |
| author_sort | Wang, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T11:49:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-97967 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:40Z |
| publishDate | 2025 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-979672025-07-22T06:02:30Z Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration Wang, S. Huang, X. Zhang, M. Bao, S. Liu, L. Fu, X. Zhang, T. Song, Yongze Kedron, P. Wilson, J., Ye, X. Yang, C. Guan, W. 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 2025 Journal Article http://hdl.handle.net/20.500.11937/97967 10.1007/s43762-025-00165-1 http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Wang, S. Huang, X. Zhang, M. Bao, S. Liu, L. Fu, X. Zhang, T. Song, Yongze Kedron, P. Wilson, J., Ye, X. Yang, C. Guan, W. Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| title | Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| title_full | Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| title_fullStr | Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| title_full_unstemmed | Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| title_short | Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| title_sort | open science 2.0: revolutionizing spatiotemporal data sharing and collaboration |
| url | http://hdl.handle.net/20.500.11937/97967 |