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Figure 2 provides an overview of the architectural design of the service implementation. Researchers access JupyterLab operated on the EGI e-Infrastructure (provided by WP9) in order to analyse primary data for the purpose of new particle formation event detection and description. JupyterLab is accessible from the corresponding D4Science Virtual Research Environment(VRE). Having cloned the required Jupyter Notebookfrom GitHub, researchers can start to analyse primary data to detect and describe new particle formation events.


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Figure 2. Architectural Design of the Service Implementation.

Workflow and Interfaces

The analysis consists of two main steps. Both are implemented as D4Science Data Miner algorithms and are accessed from within the Jupyter Notebook, programmatically via a WPS (OGC Web Process Service) interface. Given a day and place, as configured by the researcher, the first step fetches and visualizes primary data. The primary data are published by SmartSMEAR, a “data visualization and download tool for the database of continuous atmospheric, flux, soil, tree physiological and water quality measurements at SMEAR research stations of the University of Helsinki.” SmartSMEAR is developed and provided in collaboration with CSC (https://www.csc.fi/home), the Finnish national supercomputing center, who also host the SMEAR data. SmartSMEAR is thus an (software) artifact of the SMEAR(Station for Measuring Ecosystem-Atmosphere Relations) research infrastructure (RI). SmartSMEAR provides an API for data access. The primary data can thus be fetched and loaded into Python data structures in a programmatic manner.

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