Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro- Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP).

Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale / Fiore, S.; Elia, D.; Palazzo, C.; Dranca, A.; Antonio, F.; Williams, D. N.; Foster, I.; Aloisio, G.. - (2018), pp. 3226-3234. (Intervento presentato al convegno 2018 IEEE International Conference on Big Data, Big Data 2018 tenutosi a USA nel 2018) [10.1109/BigData.2018.8622205].

Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale

Fiore S.;
2018-01-01

Abstract

Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro- Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP).
2018
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
USA
IEEE
978-1-5386-5035-6
Fiore, S.; Elia, D.; Palazzo, C.; Dranca, A.; Antonio, F.; Williams, D. N.; Foster, I.; Aloisio, G.
Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale / Fiore, S.; Elia, D.; Palazzo, C.; Dranca, A.; Antonio, F.; Williams, D. N.; Foster, I.; Aloisio, G.. - (2018), pp. 3226-3234. (Intervento presentato al convegno 2018 IEEE International Conference on Big Data, Big Data 2018 tenutosi a USA nel 2018) [10.1109/BigData.2018.8622205].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/292851
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