Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in the era of Big Data and of multi-messenger analysis in astroparticle physics. We propose an open science web platform called ASTROPARTICLE.ONLINE which enables us to publish, store, search, select, and analyze astroparticle data. In the first stage of the project, the following components of a full data life cycle concept are under development: describing, storing, and reusing astroparticle data; software to perform multi-messenger analysis using deep learning; and outreach for students, post-graduate students, and others who are interested in astroparticle physics. Here we describe the concepts of the web platform and the first obtained results, including the meta data structure for astroparticle data, data analysis by using convolution neural networks, description of the binary data, and the outreach platform for those interested in astroparticle physics. The KASCADE-Grande and TAIGA cosmic-ray experiments were chosen as pilot examples.

Cosmic ray data collected by the KASCADE air shower experiment are competitive in terms of quality and statistics with those of modern observatories. We present a novel mass composition analysis based on archival data acquired from 1998 to 2013 provided by the KASCADE Cosmic ray Data Center (KCDC). The analysis is based on modern machine learning techniques trained on simulation data provided by KCDC. We present spectra for individual groups of primary nuclei, the results of a search for anisotropies in the event arrival directions taking mass composition into account, and search for gamma-ray candidates in the PeV energy domain.

During the ongoing Covid-19 pandemic, people all over the world were forced to think about new ways of interacting with each other and this has especially challenged academics in their outreach activities with pupils. New online formats needed to be developed, and we used this opportunity to design and implement an (not only) online Masterclass using data from the KASCADE experiment. The masterclass is built on the KASCADE Cosmic Ray Data Centre and uses Jupyterhub and Notebooks for data analysis. We gained first practical experience during the International Cosmic Day with students at the age of 14-19 years. The Masterclass includes lectures on cosmic ray physics and data analysis, which are then consolidated in a hands-on part. By performing a cosmic-ray composition analysis on KASCADE data, the participants gain experience in using the KCDC open data web platform, working in the Jupyter environment, preprocessing data from a real astroparticle physics experiment, programming Python and performing exploratory data analysis.

KCDC, the 'KASCADE Cosmic-ray Data Centre', is a web-based interface where initially the scientific data from the completed air-shower experiment KASCADE-Grande was made available for the astroparticle community as well as for the interested public. Over the past 7 years, we have continuously extended the data shop with various releases and increased both the number of detector components from the KASCADE-Grande experiment and the data sets and corresponding simulations. With the latest releases we added a new and independent data shop for a specific KASCADE-Grande event selection and by that created the technology for integrating further data shops and data of other experiments, like the data of the air-shower experiment MAKET-ANI in Armenia. In addition, we made available educational examples how to use the data, more than 100 cosmic ray energy spectra from various experiments, and recently attached a public server with access to Jupyter notebooks. In this paper we present a brief history of KCDC, the main features of the recent release as well as will discuss future development plans.

Many projects want to share knowledge on particle and astroparticle physics (in particular, cosmic ray physics), however multi-messenger astroparticle physics is still a young field of research and is hardly covered in educational curricula or outreach. The astroparticle.online project, founded in 2018 within the framework of the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI), encompasses an endeavor to address this issue. Within the project, scientists from Karlsruhe Institute of Technology (KIT), Irkutsk State University (ISU) and Moscow State University (MSU) developed a range of educational materials: articles, video lectures, tests, problems to solve, laboratory works and pre-trained neural networks for particle recognition. The project is supported by the KASCADE Cosmic-ray Data Center (KCDC) and GRADLCI data aggregation platform, where one can retrieve and analyze open scientific data from various experiments. The main audience of the project's activities are high school and undergraduate students. All the educational materials are available online at the project's web portal https://astroparticle. online, they are used both in online and offline masterclasses organized by the project members, and also as the supplementary content by educational organizations: for example, in the ISU course "Introduction to experimental methods in high energy astrophysics". Over the time the project has been operating, more than 150 students took part in its activities. This contribution will cover the experience gained while running the project for more than 3 years, our challenges and developments.