The Photon Neutron Data Science Demonstrator leverages on the photon-neutron community to improve computing facilities by creating a virtual platform for all users. Photons and Neutrons are widely used for research in many scientific fields and they require large Research Infrastructures (RI). Research at these RIs makes use of large-area detectors, multichannel detection, and high repetition of measurements. This leads to large quantities of data and raises the need to perform data analysis in an efficient manner. Thousands of users of the RIs propose, conduct and analyze data from scientific experiments in a wide range of application domains. Access is granted after a thorough peer-review of the scientific proposals. Often, these users’ groups are small teams of scientists coming from universities and research organizations using RIs in various locations in Europe according to the specific characteristics of the beamlines; in general, more than one analytical facility is needed for the same experiment. Critical issues are data storage, sustained access to the data and an efficient data analysis ecosystem.
Data integration and data interoperability allow users to exploit the sensitivity of multiple instruments, and are the driving force behind new discoveries. The open science enabled by this project, in combination with the EOSC ecosystem, will be a catalyst to make this happen with LOFAR (Low-Frequency Array) data as well. Existing LOFAR data will be made readily available to a much larger and broader audience, enabling novel scientific breakthroughs. Important discoveries are regularly made by re-analysing existing astronomy data.
Central to the science of hydrology is the localized nature of the medium through which water flows. The science demonstrator proposal seeks to integrate the top-down approach of the eWaterCycle project with the bottom-up approach of the SWITCH-ON one.
The demonstrator democratises access to HPC simulation codes, data and workflows for the evaluation of Earth models. It is implemented through a science gateway and offers reproducibility and provenance services for monitoring, validation and reuse of the data produced during the different phases of the analysis. VERCE’s strategy is to build upon a service-oriented architecture and a data-intensive platform delivering services, workflow tools, and software as a service, and to integrate the distributed European public data and computing infrastructures (GRID, HPC and CLOUD) with private resources and the European integrated data archives of the seismology community.
The environment-related disciplines provide many challenges around data management - both in terms of data size, data diversity and data distribution. The ENVRI Radiative Forcing Integration science demonstrator focuses on the interoperability between Observational and Climate Modeling Environmental Research Infrastructures. The scientific focus is on dynamics of greenhouse gases, aerosols and clouds and their role in radiative forcing, while the technical objectives concentrate on:
the improvement of data integration services based on metadata ontologies
model-data integration by use of HPC
innovative services to compile and compare model output from different sources, especially on semi-automatic spatiotemporal scale conversion
There are a multitude of challenges being faced in the life sciences, health, food, fishery and agriculture sectors. In cancer research, Europe has taken a technical lead within international consortia around cloud-based pan-cancer genomic analysis. This global competitive advantage can be maintained by leveraging open science analysis models around controlled access data sets developed in collaboration with researchers elsewhere in the world. These analysis frameworks could also be re-used to analyse cardiovascular and neuro-degenerative diseases as well as stimulating biotech/pharmaceutical industries to use public cancer genomic data in R&D.
This demonstrator leverages EOSC resources to enhance science reproducibility of datasets uploaded to the European Genome-phenome Archive (EGA). By doing this, the new dataset will also be made available in a FAIR manner, adding metadata according to the attributes that have been chosen to contribute the strongest to the FAIR principles. Pipelines will be developed as part of this demonstrator to automate the later process. This pilot will have a pragmatic impact by demonstrating how to make analyses portable (tools and workflows), how to increase findability, how to leverage security technologies for sensible data, how to deploy the workflow into a cloud and how to make data FAIR. It will also have a long-term impact by increasing the usability of EGA hosted data by assuring to potential users that up-to-date versions of assured quality are available to download.
CryoEM aims at developing ways to share detailed information on CryoEM image processing workflows, concentrating on those processes usually run at the Facility level. This work should increase reproducibility in Science. The idea is to write from Scipion a workflow file that fully describes the image processing steps so that they can be re-executed resulting in exactly the same results, thus making the data more FAIR. This file should go with the raw data as acquired by large facilities in Europe as well as smaller EM facilities, on the cloud where the technology employed allows it.
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