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Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository.
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This content will become publicly available on December 1, 2026
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 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.
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- PAR ID:
- 10574328
- Publisher / Repository:
- Computational Urban Science
- Date Published:
- Journal Name:
- Computational Urban Science
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2730-6852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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