Data Analytics
Jupyter based interpretation of observational data published by SMEAR to support the classification of events and processing information about them using D4Science Data Miner algorithms.
New particle formation events are a worldwide observed atmospheric phenomenon that affects human respiratory health and the global climate.
This VRE demonstrates how infrastructure can safeguard FAIR (Findable, Accessible, Interoperable, Reusable) data for a research community in aerosol science. FAIR data is on the global agenda of research infrastructure development. The VRE prototypes data analysis infrastructure where data are FAIR by Design instead of leaving it to researchers to make data FAIR.
The VRE thus weaves data FAIRness into the infrastructures’ fabric. To this effect, the VRE eliminates manual download and upload of data from and to systems; systematically catalogues data products to ensure their findability and accessibility; and uses languages for knowledge representation to ensure data interoperability.
This Virtual Research Environment (VRE) supports the classification of new particle formation events and processing information about them. It integrates the SMEAR Research Infrastructure, which provides primary data, and uses EGI Jupyter and D4Science services to support primary data interpretation and the cataloging of data derived in analysis..
Access the VREJupyter based interpretation of observational data published by SMEAR to support the classification of events and processing information about them using D4Science Data Miner algorithms.
Systematic and automated cataloguing of data products derived in analysis, including figures, event descriptions, and mean event durations using the CKAN based D4Science Catalogue.
Semantic data analytics using languages for knowledge representation to ensure data derived in analysis are interoperable, for humans and machines.
For more information about the project please visit us at http://www.envriplus.eu
Access the VRE