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Sustainable Open-Data Management for Field Research: A Cloud-Based Approach in the Underlandscape Project

Augusto Ciuffoletti, Letizia Chiti

TL;DR

The paper tackles the problem of long-term accessibility for field-based research data by proposing a cloud-centric, low-maintenance ICT architecture that relies on public cloud services and a lightweight, GitHub-hosted data workflow. It defines a GeoJSON-based dataset format with six feature types and metadata, stored as standalone files in a Master repository, and supports on-site and desk-based data creation, offline editing with a dedicated editor, and multiple delivery methods (uMap visualizations, dataset repositories, and a visitor-focused Turista app). Key contributions include the Off editor, the gaiaSplit.js conversion tool, the repo_sync.py and umap_sync.py automation scripts, and a dual delivery pathway that combines interactive maps with structured repositories to ensure both usability and longevity. The approach emphasizes sustainability by minimizing bespoke infrastructure, enabling offline operation, and maintaining data in a stable, openly accessible platform (GitHub) while planning for future scalability and integration with additional tools. This work provides a practical reference model for similar projects aiming to preserve open research data beyond project lifetimes, with broad applicability to multidisciplinary field studies and open-data ecosystems.

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.

Sustainable Open-Data Management for Field Research: A Cloud-Based Approach in the Underlandscape Project

TL;DR

The paper tackles the problem of long-term accessibility for field-based research data by proposing a cloud-centric, low-maintenance ICT architecture that relies on public cloud services and a lightweight, GitHub-hosted data workflow. It defines a GeoJSON-based dataset format with six feature types and metadata, stored as standalone files in a Master repository, and supports on-site and desk-based data creation, offline editing with a dedicated editor, and multiple delivery methods (uMap visualizations, dataset repositories, and a visitor-focused Turista app). Key contributions include the Off editor, the gaiaSplit.js conversion tool, the repo_sync.py and umap_sync.py automation scripts, and a dual delivery pathway that combines interactive maps with structured repositories to ensure both usability and longevity. The approach emphasizes sustainability by minimizing bespoke infrastructure, enabling offline operation, and maintaining data in a stable, openly accessible platform (GitHub) while planning for future scalability and integration with additional tools. This work provides a practical reference model for similar projects aiming to preserve open research data beyond project lifetimes, with broad applicability to multidisciplinary field studies and open-data ecosystems.

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.

Paper Structure

This paper contains 11 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. The data format
  4. The storage
  5. Dataset life-cycle
  6. Desk-Based Dataset Creation
  7. Dataset Editing
  8. Delivering the Dataset Content
  9. Results
  10. Discussion
  11. Conclusion

Figures (4)

  • Figure 1: The Off editor displays a list of dataset features, allowing users to individually remove, edit, or extract them. Feature types can also be modified.
  • Figure 2: The Off editor’s interface for modifying the Properties object of a POI feature. Users can edit values as free text or select predefined options for certain fields.
  • Figure 3: Rendering of a dataset on uMap. On the left the popup attached to the highlighed marker. The map is available https://github.com/prin-underlandscape/Fase1-230714-Ghivizzano
  • Figure 4: A sample of the content of a dataset-specific repository. The complete repository is available https://github.com/prin-underlandscape/Fase1-230714-Ghivizzano.