Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration
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 applie...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-025-00165-1 |
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| author | Siqin Wang Xiao Huang Mengxi Zhang Shuming Bao Lingbo Liu Xiaokang Fu Ting Zhang Yongze Song Peter Kedron John Wilson Xinyue Ye Chaowei Yang Wendy Guan |
| author_facet | Siqin Wang Xiao Huang Mengxi Zhang Shuming Bao Lingbo Liu Xiaokang Fu Ting Zhang Yongze Song Peter Kedron John Wilson Xinyue Ye Chaowei Yang Wendy Guan |
| author_sort | Siqin Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8c7cc6a24e014246a16c9ff13590eed3 |
| institution | Kabale University |
| issn | 2730-6852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj-art-8c7cc6a24e014246a16c9ff13590eed32025-01-26T12:20:09ZengSpringerComputational Urban Science2730-68522025-01-01511610.1007/s43762-025-00165-1Open science 2.0: revolutionizing spatiotemporal data sharing and collaborationSiqin Wang0Xiao Huang1Mengxi Zhang2Shuming Bao3Lingbo Liu4Xiaokang Fu5Ting Zhang6Yongze Song7Peter Kedron8John Wilson9Xinyue Ye10Chaowei Yang11Wendy Guan12Spatial Sciences Institute, University of South CaliforniaDepartment of Environmental Sciences, Emory UniversityCarilion School of Medicine, Virginia TechFuture Data Lab and China Data InstituteCenter for Geographic Analysis, Harvard UniversityCenter for Geographic Analysis, Harvard UniversityMerrick School of Business, University of BaltimoreSchool of Design and the Built Environment, Curtin UniversityDepartment of GeographySpatial Sciences Institute, University of South CaliforniaDepartment of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications and Technology, Texas A&M UniversityGeography & Geoinformation Science Department, George Mason UniversityCenter for Geographic Analysis, Harvard UniversityAbstract 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.https://doi.org/10.1007/s43762-025-00165-1Open scienceSpatiotemporal data scienceSharable dataRepeatable workflow |
| spellingShingle | Siqin Wang Xiao Huang Mengxi Zhang Shuming Bao Lingbo Liu Xiaokang Fu Ting Zhang Yongze Song Peter Kedron John Wilson Xinyue Ye Chaowei Yang Wendy Guan Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration Computational Urban Science Open science Spatiotemporal data science Sharable data Repeatable workflow |
| 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 |
| topic | Open science Spatiotemporal data science Sharable data Repeatable workflow |
| url | https://doi.org/10.1007/s43762-025-00165-1 |
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