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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 service setup including a metadata management algorithm, was implemented and tested in the D4Science gCube Virtual Research Environment provided by Task 7.1, and the final computational runtime for Near Real Time (NRT) processing (i.e. flux estimates based on raw data collected the previous day) is of about 4 minutes, similar to those required for a standard run involving only a single processing scheme. 

Science Demonstrator 3 addresses a common problem for ENVRIplus RIs (specifically observatories that build on environmental sensor networks) that data acquisition service, in particular, the preparation of data transfer prior to data transmission are often not yet sufficiently standardized. This hinders the operation of efficient cross-RI data processing routines, e.g., for data quality checking. The demonstrator showcases a service prototype that allows submitting and publishing raw observational (non-geophysical) environmental time series data in common standard formats (T-SOS XML and SSNO JSON). A messaging API (EGI ARGO) is used to perform Near Real Time (NRT) quality control procedures by an Apache Storm NRT QC Topology, which publishes the quality controlled and labelled data via a messaging output queue.

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