ECASLab is a scientific data analytics environment built on top of ECAS (the ENES Climate Analytics Service), one of the thematic services included in the EOSC-hub service portfolio, as well as one of the IS-ENES Compute Services.
It provides a scientific environment exploiting a server-side approach and integrating both data and analysis tools to support data scientists in their daily research activities.
ECASLab starts from a previous effort (OphidiaLab, developed at CMCC Foundation) with the main aim of providing a virtualized research environment for researchers. It represents the entry point for users that want to test, train, exploit the ECAS Thematic Service.
A few examples of output related to different analytics experiments implemented in the ECASLab environment.
It consists of several components like an ECAS cluster, a JupyterHub instance jointly with a large set of pre-installed Python libraries for running data manipulation, analysis, and visualization, a data publication service and a tool for the infrastructure monitoring (mainly intended for the administrators).
In order to get started with ECASLab please register here to get an account.
CMCC 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 20 TB.
In particular, about 11TB of CMIP6 data for multiple models and scenarios (e.g., historical, ssp585 and ssp245) for the precipitation variable with a high temporal resolution (hourly or daily) are immediately available for the users.
The data pool is efficiently accessible from cluster resources as well as JupyterLab.
A JupyterLab environment is equipped with a set of ready-to-use Python modules for data management, analytics, machine learning and visualization to support end-users data analysis.
Users can request new data as well as the installation of additional libraries by contacting the user support.
Login here to access and exploit the the JupyterHub environment.
A simple example about a Jupyter notebook interacting with the Ophidia instance through the PyOphidia Python class.
The Notebooks examples section lists a series of Notebooks that can be executed within the ECASLab environment to perform different kind of analysis on scientific data.