The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.

Heliophysics is the study of highly energetic events that originate on the Sun and propagate through the solar system. Such events can cause critical and possibly fatal disruption of the electromagnetic systems on spacecraft and on ground based structures such as electric power grids, so there is a clear need to understand the events in their totality as they propagate through space and time. The eScience challenge posed is that the data was gathered by many observatories and communities that have hitherto not needed to work together. Firstly, this involves the problem of helping users to more easily find and understand the relevance of data, especially data from outside their domain. Secondly, it involves solving challenges of data integration. We describe the design of the HELIO infrastructure, based on the use of Web Services linked together by workflows and accessible via portal-based user interfaces. We also discuss current progress in the implementation of this infrastructure and the feedback from the user community.

We present SphereViz, a novel 3D user interface for the visual exploration of multi-dimensional data sets in virtual reality environments. SphereViz builds on known visualization and search concepts like RadViz and RelevanceSphere. It combines them with 3D-interaction techniques like World in Miniature for projection in virtual environments. A prototype implementation of SphereViz allows to study, on one hand, the visualization methods of images in 3D space, and on the other hand, intuitive search methods and adequate interaction techniques.

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Abstract. Euclid is a Europe-led cosmology space mission dedicated to a visible and near infrared survey of the entire extra-galactic sky. Its purpose is to deepen our knowledge of the dark content of our Universe. After an overview of the Euclid mission and science, this contribution describes how the community is getting organized to face the data analysis challenges, both in software development and in operational data processing matters. It ends with a more specific account of some of the main contributions of the Swiss Science Data Center (SDC-CH). † On behalf of the Euclid collaboration 1 arXiv:1701.08158v1 [astro-ph.IM]