The ESCAPE Data Science Summer School 2021 ADASS XXXI Poster
Creators
- 1. Univ. Savoie Mont Blanc, CNRS, LAPP
- 2. Friedrich-Alexander-University Erlangen
- 3. Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
- 4. Scuola Normale Superiore, Pisa, Italy
- 5. Astroparticle Physics, TU Dortmund University, Germany
- 6. AI Center, ETH Zurich, and Institute of Neuroinformatics, University of Zurich and ETH
- 7. Erlangen Centre for Astroparticle Physics (ECAP)
- 8. University of Manchester, Manchester, United Kingdom
- 9. IRFU, CEA Paris-Saclay, Université Paris-Saclay, France
- 10. Center for Astrophysics | Havard & Smithonian, Cambridge MA, US
Description
The goal of reaching an Open Science ecosystem in the European Open-Science Cloud (EOSC) is not possible without training of early career scientists. In particular, the creation and maintenance of high-quality and open software need special consideration. This is tackled within the ESCAPE H2020 project through thematic training events: the ESCAPE Data Science Summer Schools.
During these schools, young scientists in the field of astronomy, astro-particle and particle physics are taught the necessary ingredients for their software to become a part of open science by experienced code custodians.
The 2021 school's edition was a continuation of the ASTERICS/OBELICS summer schools previously organised at LAPP (Annecy, France) but has been re-organised as an online event due to the COVID crisis. It witnessed more than 1000 registered participants, showing both the need and importance of such training events.
Following the FAIR paradigm and as an example of good practices in code development, the full information of the school are openly available online, including scientific programme, agenda and links to all contributions (software repository, notebooks, contributions, presentations and recordings).
In this contribution, we review the goals, organisation and participants feedback of such an event to present a return of experience for future similar events.
Notes
Files
escape_data_science_school_2021_adass_poster.pdf
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Additional details
Related works
- Describes
- Software: 10.5281/zenodo.5093909 (DOI)