Back to Search Start Over

The foundations of big data sharing: A CGIAR international research organization perspective

Authors :
Ashleigh M. Basel
Kien Tri Nguyen
Elizabeth Arnaud
Alessandro C. W. Craparo
Source :
Frontiers in Environmental Science, Vol 11 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

The potential of big data capabilities to transform and understand global agricultural and biological systems often relies on data from different sources that must be considered together or aggregated to provide insights. The value of data is however not only in its collection and storage, but largely in its re-use. Big data storage repositories are not enough when we consider a world brimming with escalating volumes of data, here we need to consider innovative systems and tools which address data harmonization and standardization and importantly, ones that can bridge the gap between science and end users. In this paper, we will demonstrate how CGIAR (including the Alliance of Bioversity International and CIAT) develops a culture of co-operation and collaboration among custodians of agrobiodiversity data, as well as new directions for big data. CGIAR first launched the Platform for Big Data in Agriculture to enhance the development and maintenance of its data. This helped establish workflows of cross-platform synthesis, annotate and apply the lessons learnt. The Platform then built GARDIAN (Global Agricultural Research Data Innovation and Acceleration Network)—a digital tool that harvests from ∼40 separate open data and publication repositories that 15 CGIAR centres have used for data synthesis. While there have been significant advances in big data management and storage, we also identify the gaps to improve use, and the re-use of data in order to reveal its added value in decision making.

Details

Language :
English
ISSN :
2296665X
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Environmental Science
Publication Type :
Academic Journal
Accession number :
edsdoj.7e7f79045c43e69c366626471b321e
Document Type :
article
Full Text :
https://doi.org/10.3389/fenvs.2023.1107393