Annotation
Dive into the world of effective data management with our hands-on DASI hackathon facilitated by experts from ECMWF. Explore the intricacies of the Data Access and Storage Interface from the IOSEA project, designed to leverage cutting-edge I/O hardware while maintaining a user-friendly interface for researchers. Elevate your data workflows and bridge the gap between advanced technology and scientific needs.
Benefits for the attendees, what will they learn:
- Learn about best practices for effective data management in scientific workflows
- Understand how DASI (Data Access and Storage Interface) can be used to implement these best practices
- Learn how to use the DASI in your projects
- Gain hands-on experience supported by the DASI developers and other experts from ECMWF
Level
Beginner
Language
English
Prerequisites
Basic level programming experience in Python.
Agenda and Content of the Webinar
13:30 – 13:45 Introduction to DASI (Data Access and Storage Interface)
13:45 – 14:15 How to use DASI: example use case
14:15 – 14:30 Coffee Break
14:30 – 15:15 Hands-on session: Archive Data
15:15 – 15:30 Coffee Break
15:30 – 16:00 Hands-on session: Retrieve Data
16:00 – 16:30 Q&A and wrap up
About the tutors
Metin Cakircali is a software developer at the European Centre for Medium-Range Weather Forecasts (ECMWF). He has experience in the development of 3D visualisation tools, graphical user interface (GUI), and numerical solvers in high-performance computing.
Jenny Wong is a Research Software Engineer in the Data Processing Services Team at ECMWF, primarily focusing on the development of a new framework for the statistical processing of ensemble model outputs. Her academic background is in theoretical physics, and she previously worked in the Space Weather and Atmosphere Group at the British Antarctic Survey.
Dr. James Hawkes is a Team Leader at the European Centre for Medium-Range Weather Forecasts (ECMWF), specialising in meteorological big data software development. With a background in engineering and high-performance computing, he has researched numerical method scalability extensively and holds a PhD in chaotic multigrid solvers.
Acknowledgements
This work was supported by the IO-SEA project. This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955811. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and France, Germany, the United Kingdom, Ireland, the Czech Republic, and Sweden. This project has received funding from the Ministry of Education, Youth and Sports of the Czech Republic (ID: MC2105).
This course was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).
