Abstract Every computation presents a small chance that an unexpected phenomenon ruins or modifies its output. Computers are prone to errors that, although may be very unlikely, are hard, expensive or simply impossible to avoid. In the exascale, with thousands of processors involved in a single computation, those errors are especially harmful because they can corrupt or distort the results, wasting human and material resources. In the present work, we study the effect of ionizing radiation on several pieces of commercial hardware, very common in modern supercomputers. Aiming to reproduce the natural radiation that could arise, CPUs (Xeon, EPYC) and GPUs (A100, V100, T4) are subject to a known flux of neutrons coming from two radioactive sources, namely $$^{252}$$ 252 Cf and $$^{241}$$ 241 Am-Be, in a special irradiation facility. The working hardware is irradiated under supervision to quantify any appearing error. Once the hardware response is characterised, we are able to scale down the radiation intensity and to estimate the effects on standard data centres. This can help administrators and researchers to develop their contingency plans and protocols.

In this work the strategy of the Latin American Giant Observatory (LAGO) to build a Latin American collaboration is presented. Installing Cosmic Rays detectors settled all around the Continent, from Mexico to the Antarctica, this collaboration is forming a community that embraces both high energy physicist and computer scientists. This is so because the data that are measured must be analytical processed and due to the fact that a priori and a posteriori simulations representing the effects of the radiation must be performed. To perform the calculi, customized codes have been implemented by the collaboration. With regard to the huge amount of data emerging from this network of sensors and from the computational simulations performed in a diversity of computing architectures and e-infrastructures, an effort is being carried out to catalog and preserve a vast amount of data produced by the water-Cherenkov Detector network and the complete LAGO simulation workflow that characterize each site. Metadata, Permanent Identifiers and the facilities from the LAGO Data Repository are described in this work jointly with the simulation codes used. These initiatives allow researchers to produce and find data and to directly use them in a code running by means of a Science Gateway that provides access to different clusters, Grid and Cloud infrastructures worldwide.

Data Science is an increasingly popular area of Knowledge Discovery and Data Mining. Leading consumer Web companies such as Amazon, Facebook, eBay, Google and LinkedIn, as well as B2B companies like Salesforce, possess Petabytes of data. Through effective mining of this data, they create products and services that benefit millions of users and generate tremendous amount of business value. It is widely acknowledged that Data Scientists play key roles in the creation of these products, from pattern identification, idea generation and product prototyping to experiment design and launch decisions. Nonetheless, they also face common challenges, such as the gap between creating a prototype and turning it into a scalable product, or the frustration of generating innovative product ideas that do not get adopted. Organizers of this tutorial have many years of experience leading Data Science teams in some of the most successful consumer Web companies. In this tutorial, we introduce the framework that we created to nurture data-driven product innovations. The core of this framework is the focus on scale and impact - we take the audience through a discussion on how to balance between velocity and scale, between product innovation and product operation, and between theoretical research and practical impact. We also share some guidelines for successful data-driven product innovation with real examples from our experiences.