2022-11-20, 15:45–16:00 (Europe/Athens), Room II
Presentation of the integration of CERN's data management and orchestration open-source tool (Rucio) into CERN's reproducible analysis open-source platform (Reana). By leveraging this integration, scientists, not only from CERN, are now able to fetch files for analysis directly from a Rucio instance to a Reana cluster without the need to upload them from local storage; this concept is fundamental to sharing analysis workflows and making them reproducible across different research centers.
Today, half of the researchers cannot reproduce their own results. The reproducibility of scientific results is becoming more and more crucial for the advancement of science and for testing new theories, as well as being useful to spark scientific collaboration. The aim is to expand and adapt the current capabilities to enable scientists to flexibly respawn complete analyses and interact with large open data sets.
Thanks to recent advances in container technologies in the general IT industry, extended portability can be achieved while preserving analysis knowledge, like data and code, and capturing analysis assets in digital repositories to facilitate their future reuse.
(Online Talk)
In this talk, Agisilaos will describe the integration of CERN's data management and orchestration open source tool (Rucio) into CERN's reproducible analysis open-source platform (Reana), as part of his work during the CERN Openlab Summer Student program. By leveraging this integration, scientists, not only from CERN, are now able to fetch files for analysis directly from a Rucio instance to a Reana cluster without the need to upload them from local storage; this concept is fundamental to sharing analysis workflows and making them reproducible across different research centers.
Agisilaos is a Computer Engineering & Informatics graduate from the University of Patras, Greece. In the past, he has been an intern for CERN, Amazon, and Microsoft and has participated twice in Google Summer of Code with Mozilla and KDE. He has attended various coding competitions, and he is a recipient of several distinctions from some of the most prestigious hackathons in the world. He has also collaborated with EPFL and Inria Nancy Research Centre and co-authored two research papers. His recent research interests include privileged learning methods for learning robot controllers, the area of focus of his thesis.