Data Science Environments

Technologies available

CodiMD Voilà Quantstack JupyterLab

Accessible via the web interface at the remote sites of researchers to enable them to work on algorithms and data processing programs interactively.

Data Science Environments
This is all around the integration of data science environments into the federated Science Mesh, in order to provide facilitate collaborative research and enable cross-federation sharing of computational tools, algorithms and resources.

Objective: Users will be able to access remote execution environments to replay (and modify) analysis algorithms without a need to set up upfront accounts in the remote system.

The functional integration with EFSS (Enterprise File Sync and Share) such as

  • interactive features: advance from current JupyterHub to JupyterLab with collaborative notebook editing, explore interactive widgets such as provided by QuantStack Voila, etc.
  • Jupyter native- interfaces for OCM sharing
  • connection to code repositories such as Git-based or CVMFS-based and lightweight runtime environments similar to
  • interface to computational resources (such as BigData Spark, HPC, batch and Grid clusters).


Use cases



Find out more about Data Science Environments in our webinar

Watch the recording