|Time:||June 1, 2022, 4:00 p.m. (CEST)|
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Please note that this SIGDIUS Seminar starts at 4pm instead of 2 pm as usual!!
The Special Interest Group Data Infrastructure offers a forum to interested working groups that want to set up or further develop an RDM infrastructure at working group or institute level. We invite you to a monthly SIGDIUS seminar, to which we invite internal and external experts for presentations and discussions. SIGDIUS members will have the opportunity to exchange their experiences with concrete RDM infrastructures.
We cordially invite all interested parties to our next meeting on 1 June 2022 at 4 pm. This seminar will be held as an online seminar. For participation, please send an e-mail to Juergen.Pleiss@itb.uni-stuttgart.de.
(Swiss Data Science Center)
Renku: a platform for collaborative and reproducible data analysis
The Renku platform developed at the Swiss Data Science Center integrates state-of-the-art data science and open source software tools to enable collaborative, reproducible and reusable data science. Data, code, workflows and computational environments in every project are versioned from the start, allowing researchers to focus on their task of discovery rather than worrying about preserving their work. In addition, Renku provides a hosted solution for running those computational environments, which allows researchers to share their fully reproducible work instantly with others. Renku treats all aspects of the data analysis process (code, data, workflows) as nodes in an indexed, searchable Knowledge Graph, meaning that datasets, algorithms or full workflows can be discovered, shared and reused. In this talk I will describe the basic aspects of Renku and its architecture, as well as illustrate some on-going use-cases.
Rok Roškar obtained a B.A. in Physics from Washington University in St.Louis in 2003. After obtaining his PhD in theoretical Astrophysics from the University of Washington in 2010, Rok spent several years as a Postdoctoral researcher at the Institute for Computational Science, University of Zürich. Seeking new challenges, he moved to the ETH Scientific IT Services group, where he helped researchers across different ETH domains solve their (big) data analysis problems. He specialized in optimizing and scaling up data analysis tasks by mapping them to high-performance computing resources. Since July 2017 he has been at the Swiss Data Science Center developing Renku, the Center's data science platform