Data Science environments in ScienceMesh

About

Data Science is a complex field that requires a high level of expertise and collaboration among teams of experts. With the rise of big data, it has become increasingly important to create collaborative workflows that enable data scientists to combine their skills and knowledge to create better results. This, however, can be a challenge in an environment of heterogenous cloud and storage systems.

ScienceMesh, developed in CS3MESH4EOSC project, creates the Federated Scientific Mesh providing federated sharing of data across different sync-and-share services, federated use of applications (such as collaborative document editing, data archiving, and data publishing), fast transfer of large datasets and remote data analysis (Data Science environments).

For ScienceMesh distributed data science environments we developed a JupyterLab extension, integrating with ScienceMesh – so that we can provide file browsing and additional share and collaboration functionalities for notebooks and resources.

This talk will present the development of Data Science environments in ScienceMesh and demonstrate how it supports collaborative workflows in federated sync-and-share environment.