Journal article Open Access

Mapping heterogeneous research infrastructure metadata into a unified catalogue for use in a generic virtual research environment

Martin, Paul; Remy, Laurent; Theodoridou, Maria; Jeffery, Keith; Zhao, Zhiming

Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers

in data science by integrating tools for data discovery, data retrieval, workflow management

and researcher collaboration, often coupled with a specific computing infrastructure. Recently, the push

for better open data science has led to the creation of a variety of dedicated research infrastructures

(RIs) that gather data and provide services to different research communities, all of which can be used

independently of any specific VRE. There is therefore a need for generic VREs that can be coupled

with the resources of many different RIs simultaneously, easily customised to the needs of specific

communities. The resource metadata produced by these RIs rarely all adhere to any one standard

or vocabulary however, making it difficult to search and discover resources independently of their

providers without some translation into a common framework. Cross-RI search can be expedited by

using mapping services that harvest RI-published metadata to build unified resource catalogues, but

the development and operation of such services pose a number of challenges.

In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata

Portal, which uses X3ML mappings to build a single catalogue for describing data products and other

resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference

Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard

to structure its catalogued metadata. We consider the extent to which it addresses the challenges of

cross-RI search, particularly in the environmental and earth science domain, and how it can be further

augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic

search across multiple domains of discourse.

Files (1.6 MB)
Name Size
2019.journal.fgcs.semantic-mapping-camera.pdf
md5:bdfaa2ea29ac7ed09c2398e650634749
1.6 MB Download
162
160
views
downloads
Views 162
Downloads 160
Data volume 250.7 MB
Unique views 157
Unique downloads 154

Share

Cite as