Main result: the Integration of the Latin American Giant Observatory (LAGO) processes of generation and publishing of simulated data within EOSC following the FAIR principles.
From the beginning of the LAGO Collaboration, several threats were identified related to the management of the generated data and shared resources: i) the lack of computational and storage resources having a common accessibility method and without compatibility issues among platforms; ii) the absence of effective mechanisms for the curation and sharing of the generated data; and iii) the difficulty of coordinating the development, tracking and deploying the official releases of codes.
So, thanks to the integration of their computing frameworks into the EOSC portfolio of services will largely enhance the scientific and social impact of LAGO. For example, it is now able to cite, reutilise and publish the results obtained by other communities. Scholarly has been made more easy as well the development cycle, because adoption and modification of the software is standardised.
Addressing target audiences and expressing needs
- Use of research Infrastructure
- Collaboration
- Fellowship to advance my/our research
- On-demand computational infrastructure for big research challenges: > 2.4M CPU/hours consumed since January 2021.
- Permanent Storage for primary copies and replicas/backups compatible with publishing: > 16TB currently used for final datasets
- Tools for publishing datasets, searching engines and testing FAIR: currently 428 datasets freely available after the embargo period.
- Open tools for CD/CI, documentation and coordination for maintaining our software.
- (Currently in development, to make use of GPUs and AI/BigData platforms).
- Others/ No specific audience
- Research and Technology Organisations
- Academia/ Universities
R&D, Technology and Innovation aspects
It is expected that LAGO TS will manage the complete data workflow in LAGO Collaboration. There are several kinds of data: L0,1,2 for detector acquisitions, cleaning and processing, plus S0,1,2 for simulated and analysed ones. These could be increased if new AI techniques are used. LAGO TS is currently supporting S0 data generation with a TRL 8, while the S1 analysis is in a TRL 6-7 state.
Simulation, analysis and processing data are scalable by definition through cloud infrastructures. As a demonstration, the service has consumed > 2.4M CPU/hours since January 2021 on EOSC FedCloud. On the other hand, every new calculation directly allows communicating results ( >20 papers/presentations/posters related since 2021 and many pending). It will allow LAGO publishing as large Collaborations (Cern, Auger, etc) that continuously produce scheduled papers. Additionally, as a consequence of following FAIR guidelines we can take advantage of Reutilisation. It enables AI/BigData research and allows new findings (hidden knowledge in data). Furthermore, Reutilisation and Reproducibility enlarge the number of expected customers from the LAGO community (>100) to the Astrophysics community (>1,000), the Muongrapy and Volcanology (>100) and HEP (>1000).
Results obtained are completely replicable and reusable due to the application of FAIR principles to the software as well as data. Other researchers can use the code with the lesser restrictions of BSD-3-Clause and even the pre-configured Docker images to reproduce the simulations or to compare the analysis/processing previously done by other scientists. Moreover, the datasets are published under the Creative Commons licence (CC BY-NC-SA 4.0).
The sustainability of this research is guaranteed by the long establishment of the LAGO Collaboration during 15 years, with 11 countries, 30 institutions and >100 members involved and growing and, additionally, with the external collaborators and scholars. With respect to the Thematic Service itself, the long-term sustainability is assured by the quality mechanisms and standardised metrics adopted during the EOSC-Synergy project. They enable continuing with the development independently if any author leaves the collaboration. The use of standards in code, CD/CI and metadata allows joining other authors for continuously improving the LAGO Thematic Service.
