Abstract
The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated in EOSC, towards addressing the diverse astrophysics user communities needs for data management, mapping and structure detection. These services relies on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and, injecting machine learning techniques, for detection of structures in large scale multidimensional maps.
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Change history
28 August 2020
In the original version of the book, the following belated correction has been incorporated: The affiliation of Mario Raciti has been changed from “INAF, Catania Astrophysical Observatory, Catania, Italy” to “Università di Catania, Dipartimento di Matematica e Informatica, Catania, Italy”.
Notes
- 1.
- 2.
SKA web page: https://www.skatelescope.org/.
- 3.
EOSCpilot web page: https://eoscpilot.eu/.
- 4.
ViaLactea Science Gateway: https://vialactea-sg.oact.inaf.it/.
- 5.
EGI Check-in service: https://www.egi.eu/services/check-in/.
- 6.
EGI Federated Cloud: https://www.egi.eu/services/cloud-compute/.
- 7.
EGI Applications Database: https://appdb.egi.eu/store/vappliance/visivo.sd.va.
- 8.
NEANIAS web page: https://www.neanias.eu/.
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Acknowledgments
The research leading to these results has received funding from the European Commissions Horizon 2020 research and innovation programme under the grant agreement No. 863448 (NEANIAS).
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Sciacca, E. et al. (2020). Towards Porting Astrophysics Visual Analytics Services in the European Open Science Cloud. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_43
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