# German-Russian Astroparticle Data Life Cycle Initiative

**Authors:** Andreas Haungs, Igor Bychkov, Julia Dubenskaya, Oleg Fedorov, Andreas, Heiss, Donghwa Kang, Yulia Kazarina, Elena Korosteleva, Dmitriy Kostunin,, Alexander Kryukov, Andrey Mikhailov, Minh-Duc Nguyen, Frank Polgart,, Stanislav Polyakov, Evgeny Postnikov, Alexey Shigarov, Dmitry Shipilov, Achim, Streit, Victoria Tokareva, Doris Wochele, J\"urgen Wochele, Dmitry Zhurov

arXiv: 1907.13303 · 2019-08-01

## TL;DR

The paper discusses the development of a comprehensive data life cycle system for astroparticle physics, integrating data processing, sharing, and analysis across international collaborations to enhance research and education.

## Contribution

It introduces the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI), a novel project creating a prototype for a unified data management system tailored for astroparticle physics.

## Key findings

- Development of a DLC prototype for astroparticle physics data
- Integration of distributed data storage and machine learning methods
- Enhancement of multi-messenger data analysis capabilities

## Abstract

A data life cycle (DLC) is a high-level data processing pipeline that involves data acquisition, event reconstruction, data analysis, publication, archiving, and sharing. For astroparticle physics a DLC is particularly important due to the geographical and content diversity of the research field. A dedicated and experiment spanning analysis and data centre would ensure that multi-messenger analyses can be carried out using state-of-the-art methods. The German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) is a joint project of the KASCADE-Grande and TAIGA collaborations, aimed at developing a concept and creating a DLC prototype that takes into account the data processing features specific for the research field. An open science system based on the KASCADE Cosmic Ray Data Centre (KCDC), which is a web-based platform to provide the astroparticle physics data for the general public, must also include effective methods for distributed data storage algorithms and techniques to allow the community to perform simulations and analyses with sophisticated machine learning methods. The aim is to achieve more efficient analyses of the data collected in different, globally dispersed observatories, as well as a modern education to Big Data Scientist in the synergy between basic research and the information society. The contribution covers the status and future plans of the initiative.

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/1907.13303/full.md

## References

6 references — full list in the complete paper: https://tomesphere.com/paper/1907.13303/full.md

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Source: https://tomesphere.com/paper/1907.13303