Data Aggregation Platform for Experiments of Astroparticle Physics

The big data revolution has overturned well-established ap-proaches to data analysis and intensified the demand for access to het-erogeneous data. Modern developed methods allow for the extraction ofnew knowledge from the data, which enables researchers to approachmany previously unsolved mysteries. This trend, observed in many areasof human activity, is also tangible in the field of astroparticle physics.Combined analysis of various experimental data allows researchers toderive deeper insights into the processes occurring in the universe andextend the borders of our knowledge about nature.Providing the infrastructure for such investigations is a topical issue ofthe astroparticle physics community.In this report we examine a service for the aggregated retrieval of hetero-geneous data from distributed storages of numerous astroparticle physicsexperiments. We describe its architecture, available data, principles offunctioning and interaction with users and data center

An Analysis Framework for KCDC

We will introduce a data analysis extension for the KAS-CADE Cosmic-ray Data Center (KCDC), based on the Jupyterhub/notebookecosystem. A user-friendly interface, easy access to data from KCDC, andmodern analysis software are of special interest. This contribution willdiscuss the service architecture, followed by a brief usage exampl

German-Russian Astroparticle Data Life Cycle Initiative to foster Big Data Infrastructure for Multi-Messenger Astronomy

Challenges faced by researchers in multi-messenger astroparticle physics include: computing-intensive search and preprocessing related to the diversity of content and formats of the data from different observatories as well as to data fragmentation over separate storage locations; inconsistencies in user interfaces for data retrieval; lack of the united infrastructure solutions suitable for both data gathering and online analysis, e.g. analyses employing deep neural networks. In order to address solving these issues, the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) was created. In addition, we support activities for communicating our research field to the public. The approaches proposed by the project are based on the concept of data life cycle, which assumes a particular pipeline of data curation used for every unit of the data from the moment of its retrieval or creation through the stages of data preprocessing, analysis, publishing and archival. The movement towards unified data curation schemes is essential to increase the benefits gained in the analysis of geographically distributed or content-diverse data. Within the project, an infrastructure for effective astroparticle data curation and online analysis was developed. Using it, first results on deep-learning based analysis were obtained

Additional file 5: of Discrimination of three genetically close Aspergillus species by using high resolution melting analysis applied to indoor air as case study

Melting temperature obtained for the species from the Versicolores group (XLSX 11 kb