In this report, we introduce a novel approach to visualize extremely large graphs efficiently. Our method combines two force-directed algorithms, Kamada-Kawai and ForceAtlas2, to handle different graph components based on their node count. Additionally, we suggest utilizing the Fast Multipole method to enhance the speed of ForceAtlas2. Although initially designed for analyzing bitcoin transaction graphs, for which we present results here, this algorithm can also be applied to other crypto currency transaction graphs or graphs from diverse domains.

In recent years, science has relied more than ever on large-scale data as well as on distributed computing and human resources. Scientists and research engineers in fields such as climate science and computational seismology, constantly strive to make good use of remote and largely heterogeneous computing resources (HPC, Cloud, institutional or local resources, etc.), process, archive and analyse results stored in different locations and collaborate effectively with other scientists.

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines. Keywords-Science Gateway, HPC, Data-Intensive, Data Science, Metadata and Storage, solid-Earth Sciences, Virtual Research Environment, e-Infrastructure.

The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.

Developpement et deploiement d'une application desktop et mobile sur une mini grille de calcul a l'Universite de Kinshasa pour le traitement des donnees GPS

Advanced application environments for seismic analysis help geoscientists to execute complex simulations to predict the behaviour of a geophysical system and potential surface observations. At the same time data collected from seismic stations must be processed comparing recorded signals with predictions. The EU-funded project VERCE ( http://verce.eu/ ) aims to enable specific seismological use-cases and, on the basis of requirements elicited from the seismology community, provide a service-oriented infrastructure to deal with such challenges. In this paper we present VERCE’s architecture, in particular relating to forward and inverse modelling of Earth models and how the, largely file-based, HPC model can be combined with data streaming operations to enhance the scalability of experiments. We posit that the integration of services and HPC resources in an open, collaborative environment is an essential medium for the advancement of sciences of critical importance, such as seismology.

The Earth Science (ES) community has three major IT related concerns: Modeling (computing intensive), exploitation of datasets, and production of large shared datasets. All could be accomplishable using Grid. Different Grid middleware solutions exist and are being developed. In DEGREE (Dissemination and Exploitation of Grids in Earth sciencE) the aim was to disseminate and promote uptake of Grid in ES and to create a bridge between ES and Grid communities. DEGREE identified key ES requirements and has disseminated these to Grid projects, evaluated Grid middleware tools and standards regarding ES requirements and provided feedback to Grid developers. In order to convey requirements to the Grid community, test suite specifications were developed. Test suites provide real applications for testing functional and non-functional aspects of Grid, contrary to typical whiteboard tests or use cases. In this paper the GOMEVAL test suite is explained in detail and the results obtained are discussed.