D9.2 Service deployment in computing and data e-Infrastructures Version 2

• Main Authors: Chen, Yin, Haggström, Ingemar, Buck, Justin, Stocker, Markus, Carval, Thierry, Vitale, Domenico, Huber, Robert, Hellström, Margareta, Candela, Leonardo, Haslinger, Florian
• Format: Report
• Language: unknown
• Published: Zenodo 2018
• Subjects: EISCAT
• Online Access: https://doi.org/10.5281/zenodo.3258530

This document reports on implementation results of T9.1 use cases for testing and validating ENVRIplus Theme 2 service solutions. It is an update of the previous report D9.1 that was submitted on M28. In D9.1 we explained the approach to the service integration and validation. The driver was to define appropriate use cases that address community’s real needs, integrate ENVRIplus Theme2 services as part of the service solutions, and implement those use cases following an agile approach. This document describes 7 well-developed use cases that are selected as the final Science Demonstrators. By Science Demonstrator we mean “a showcase of a service solution illustrated through a prototype implementation, which serves as proof or evidence that the Theme2 services can bring added value for supporting ENVRIplus community to deliver scientific research”. Science Demonstrator 1 addresses a requirement of the EISCAT RI community, namely to allow individual scientists to process their experimental data using their own algorithms. The challenge is common to many ENVRIplus RIs, where data is often processed using standard models and methods. As researchers want to use different analysis models, easily modify parameters or algorithms, and collaborate with each other, they need a Virtual Research Environment (VRE). This demo showcases a model making use of the D4Science gCube platform developed by T7.1, which enables scientific researchers to re-process data by implementing and adapting algorithms and parameters from other sources. Science Demonstrator 2 showcases a novel implementation of a computationally efficient tool for processing of Eddy Covariance (EC) data which offers to users the possibility to calculate EC fluxes through the EddyPro® software (LI-COR Biosciences, 2017; Fratini and Mauder, 2014) according to 4 processing schemes resulting from a different combination of existing methods. To reduce the computational runtime required, the 4 processing schemes were implemented and executed in parallel mode. The whole ...