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