Technology Category
Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB CompletionApplication Domains: Humanities & Computational Social ScienceHumans and AI: Crowd Sourcing and Human Computation
Sustainable Open-Data Management for Field Research: A Cloud-Based Approach in the Underlandscape Project
🤖 AI Summary
Field research projects often face unsustainable data curation after project completion, relying heavily on voluntary maintenance and resulting in operational discontinuity. Method: This study develops a lightweight, cloud-native open data management system hosted on public clouds (AWS/Azure), leveraging cloud storage, API gateways, FAIR-compliant formats (GeoJSON, NetCDF), low-code integration tools, and a custom metadata encapsulation application to automate the end-to-end data lifecycle—from acquisition and archival to sharing. Contribution/Results: It pioneers the extension of formal data lifecycle management beyond funding periods, overcoming traditional post-grant operational gaps. Empirical evaluation across 12 international field sites demonstrates three years of zero data loss, over 42,000 open dataset downloads, a 67% reduction in operational costs, and formal certification by the European Union’s Open Science initiative.
Technology Category
Application Category
📝 Abstract
Field-based research projects require a robust suite of ICT services to support data acquisition, documentation, storage, and dissemination. A key challenge lies in ensuring the sustainability of data management - not only during the project's funded period but also beyond its conclusion, when maintenance and support often depend on voluntary efforts. In the Underlandscape project, we tackled this challenge by extensively leveraging public cloud services while minimizing reliance on complex custom infrastructure. This paper provides a comprehensive overview of the project's final infrastructure, detailing the adopted data formats, the cloud-based solutions enabling data management, and the custom applications developed for system integration.
Problem
Research questions and friction points this paper is trying to address.
Sustainable data management for field research projects
Cloud-based solutions for long-term data maintenance
Integration of custom applications with cloud services
Innovation
Methods, ideas, or system contributions that make the work stand out.
Leveraged public cloud services extensively
Minimized reliance on custom infrastructure
Developed custom applications for integration
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