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.
Access to HPC facilities is vitally important to the fusion community, not only for plasma modelling but also for advanced engineering and design, materials research, uncertainty quantification and advanced data analytics for engineering operations (e.g. condition monitoring). The requirements for HPC class machines is expected to only increase as the community prepares for the next generation facility, ITER. However, access to HPC class infrastructure is quite restricted and obtaining time on this class of machine for algorithm development, testing and optimisation is already problematic. A few fusion centres have local access to smaller HPC class clusters but larger-scale workflows and smaller fusion research centres are required to competitively bid for time on hardware such as PRACE Tier-0 and Tier-1 facilities, often also requiring visiting the centre. Within this demonstrator, we propose to make HPC class machines available as a cloud-like service to the fusion community.
Funding agencies today require (FAIR) Data Management Plans, explaining how data acquired or produced will be preserved for re-use, sharing and verification of results. The preservation of data from CERN’s Large Hadron Collider poses significant challenges: not least in terms of scale. The purpose of this demonstrator is to show how existing, fully generic services can be combined to meet these needs in a manner that is discipline agnostic, i.e. can be used by others without modification.
The pufferfish Lagocephalus sceleratus has entered the Mediterranean Sea through the Suez Canal and is highly toxic. The goal of the multidisciplinary team of scientists who studied it was to predict the areas this species was going to invade in the near future. All the services used, the methodology and the results are discoverable and accessible through the EOSC portal.
Human language is ambiguous and often complex to interpret. One sentence can have multiple meanings. Transforming the language into real, directly usable research data, requires deep insight in the linguistic content, e.g. via dictionaries and language models. This use case explains how the integration of the CLARIN infrastructure into the EOSC portal can facilitate the study of language data and how the Portal itself can support the Social Sciences and Humanities (SSH) community.
ICOS is a pan-European research infrastructure for quantifying and understanding Europe’s greenhouse gas (GHG) balance. Its mission is to collect high-quality observational data and to promote its use, e.g. to model GHG fluxes or to support verification of emission data. ICOS brings together 120+ measurement stations across the atmosphere, ecosystem and marine domains.
The research life-cycle made easy using OpenAIRE and EOSC-hub services: making data open yet anonymous. Becky is an early career social scientist. She is excited to have been offered a competitive post in a well-established department to work on an international EC Horizon 2020 5-year research project with many partners. The project started a year before she arrived, and her institution leads the research on the language used to describe immigration in the national press.
The EOSC portal has been jointly developed and maintained by the eInfraCentral, EOSC-hub, EOSCpilot and OpenAIRE-Advance projects funded by the European Union’s Horizon 2020 research and innovation programme with contribution of the European Commission.