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Scientific article
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English

SwissEnvEO: A FAIR National Environmental Data Repository for Earth Observation Open Science

Contributors

Giuliani, Gregory; Cazeaux, Hugues; Burgi, Pierre-Yves; Poussin, Charlotte; Richard, Jean-Philippe; Chatenoux, Bruno

Published in

Data Science Journal, vol. 20, no. 22

Publication date

2021

Abstract

Environmental scientific research is highly becoming data-driven and dependent on high performance computing infrastructures to process ever increasing large volume and diverse data sets. Consequently, there is a growing recognition of the need to share data, methods, algorithms, and infrastructure to make scientific research more effective, efficient, open, transparent, reproducible, accessible, and usable by different users. However, Earth Observations (EO) Open Science is still undervalued, and different challenges remains to achieve the vision of transforming EO data into actionable knowledge by lowering the entry barrier to massive-use Big Earth Data analysis and derived information products. Currently, FAIR-compliant digital repositories cannot fully satisfy the needs of EO users, while Spatial Data Infrastructures (SDI) are not fully FAIR-compliant and have difficulties in handling Big Earth Data. In response to these issues and the need to strengthen Open and Reproducible EO science, this paper presents SwissEnvEO, a Spatial Data Infrastructure complemented with digital repository capabilities to facilitate the publication of Ready to Use information products, at national scale, derived from satellite EO data available in an EO Data Cube in full compliance with FAIR principles.

Keywords

  • Data Sharing
  • Interoperability
  • Earth Observations
  • Data Cube
  • Environmental monitoring
  • Switzerland
  • Open Science
  • Reproducibility

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

GIULIANI, Gregory et al. SwissEnvEO: A FAIR National Environmental Data Repository for Earth Observation Open Science. In: Data Science Journal, 2021, vol. 20, n° 22. doi: 10.5334/dsj-2021-022

Main files (1)

Article (Published version)
CC BY

Identifiers

Additional URL for this publication

http://datascience.codata.org/articles/10.5334/dsj-2021-022/

Journal ISSN

1683-1470
396views
497downloads

Technical informations

Creation

31/05/2021 13:47:00

First validation

31/05/2021 13:47:00

Update

24/10/2025 13:29:23

Status update

24/10/2025 13:29:23

Last indexation

31/08/2026 11:38:51
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