The ENES Data Space delivers an open, scalable and cloud-enabled data science environment for climate data analysis on top of the EOSC Compute Platform. It provides both storage and computational capabilities.

It consists of a JupyterLab instance jointly with a large set of pre-installed Python libraries and a ready-to-use Ophidia HPDA framework instance for running data manipulation, analysis and visualization.

The ENES Data Space hosts (open) data from the ESGF federated data archive on compute cloud to support researchers in realistic climate model analysis experiments.

In order to get started with the ENES Data Space please register an EGI account and join the ENES VO.
You can find further information in the Access section.

Data pool and tools

The ENES Data Space provides access to a set of specific CMIP variable-centric collections. Data are downloaded and kept in sync with the ESGF federated data archive within a disk space of about 150 TB.
In particular, about 30TB of CMIP6 data for multiple models, scenarios (e.g., historical, ssp245, ssp370 and ssp585) and variables (air temperature, precipitation, near-surface wind, specific and relative humidity) with different temporal resolutions (yearly, monthly, daily, 6-hourly) are immediately available for the users.

A JupyterLab environment is equipped with a set of ready-to-use Python modules for data management, analytics and visualization to support users data analysis.

You can exploit the intake-esm data cataloging utility for searching and discovery of the available CMIP6 datasets and loading assets into xarray datasets as well as into the Ophidia workspace.

Login here to access and exploit the JupyterLab environment.


EGI-ACE receives funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement number 101017567.

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