HPC-DAT trains NES researchers to make effective and easier use of high performance computing systems necessary to address fundamental research questions. We will organize multi-day hackathons, dedicated training events and build upon the established Python-PyData-Jupyter-Dask ecosystem. Close collaboration with researchers is achieved through selected use cases, a broad user’s committee and an open user forum. The hackathon approach will furthermore enable the project to directly engage with existing and future users from a broad set of disciplines and their evolving needs for large-scale computing.

Synthetic data is a dataset with (more or less) the same properties as an original dataset but without privacy-sensitive information. By making synthetic data available instead of (or prior to) the actual dataset, scientists gain faster and easier access to confidential data. In this project, two tools for creating synthetic data are used to unlock existing datasets, including datasets archived at DANS.

Many scientific disciplines are presently undergoing technological revolutions that lead to a common challenge: managing a distributed data explosion. Detectors, medical imaging instruments, micro-arrays, and multi-sensor instruments are producing amounts of data that are rapidly exceeding the capacities of their current local data storage and computing environments. In many cases these ?exploding data? are distributed from the very start, being produced by different research groups or distributed sensor networks. Consider examples like genome and protein analysis data produced by many research labs in the world, biobanks containing patient data from a variety of hospitals, biodiversity data collected at the banks of the river Waal, historical archives and text corpora in many different places. Combining these datasets allows for completely new forms of research. Moreover, experiments generating petabytes of data per year, such as LOFAR in radio-astronomy and CERN in particle physics, need more data processing power than ever can be located in a single facility, with data utilized by researchers all over the world. From an ICT perspective, these data have similar properties: all require reliable storage, comprehensive archiving, secure coupling and sharing. We propose to build and roll out a nation-wide grid-based e-Science infrastructure, BIG GRID, that strengthens the international position of the Netherlands in many scientific areas. BIG GRID encompasses data storage facilities and data processing services, enabled by grid services, for a requested budget of 30 M€ over a four-year period. The science case for this proposal is the integral of many different science cases, reflecting the broad scientific community base. The realization of BIG GRID is crucial to the success and continuity of many Dutch research communities, covering important areas such as life sciences, astronomy, particle physics, meteorology, and climate research, water management, to name just a few. However, the very nature of the new infrastructure, a multidimensional collaboration enabler and accelerator, allows for direct participation of also social sciences, humanities, and even addresses communities in administrative domains, like digital academic repositories. One basic ingredient for the proposed infrastructure is the network. The Netherlands are already in an excellent position, due to the world-class network services provided by SURFnet, the upgrade of which has been secured from GigaPort-NG project. BIG GRID provides opportunities for enhanced international visibility. Dutch participation in international generic grid developments is already prominent (in flagship projects like EGEE and DEISA) and are on a national scale very well covered by the VL-e project. Coordinated by the Netherlands Genomics Initiative, NBIC is the key player for enabling informatics methodology for life sciences. While the Netherlands is a leading player in the development of the grid, and has considerable expertise in bio-informatics, distributed sensors networks, and particle physics, the large-scale infrastructure to fully exploit this leading position is missing. The purpose of this proposal is to realize a science-wide national grid infrastructure. This puts the Netherlands at the forefront of grid developments, enabling many national ambitions. It enhances the excellent position of Dutch academic hospitals in patient data collections using the grid for biobanking. It enables major advances in drug discovery through combining data and through availability of massive compute resources for modelling. It allows industrial research labs, such as Philips, to both contribute to and profit from the available resources for engineering sciences. It positions LOFAR as the European centre for serving a variety of scientific communities using LOFAR data and the Netherlands as one of the Tier-1 sites for CERN?s LHC experiments. This proposal is a collaborative effort of NCF, NBIC and NIKHEF.

The Dutch-built Low-Frequency Array (LOFAR) is a unique radio telescope that brings together the signals from tens of thousands of antennas spread across the Netherlands and Europe. By removing various bottlenecks in data transport and data processing, we will unlock the full potential of LOFAR to make both sharp and wide-field images of radio waves arriving from outer space. We will study, for example, how stars form over cosmic time and how exoplanets are influenced by their parent star. We will also capture rare explosions from merging stars and study the extremes of the Universe.

Machines have long helped us understand the world around us, but only recently are we relying on their assistance in recognising patterns. Still, such machine learning has rapidly become an integral part of society, in speech recognition or information retrieval; and in science, for detecting patterns in nature and the unvarying cosmos. Now, the need is growing rapidly to run machine-learning neural networks in real time. Self-driving cars and responsive manufacturing require fast feedback for complex systems. On a more fundamental level, self-learning machines help us unveil a dynamical Universe we did not know existed. Bright, millisecond duration transients appear all over the radio sky, pointing to extreme energies. Gravitational waves whipped up by merging black holes and neutron stars peak for 0.1 second, maybe once per year. Our overall aim is to understand how such real-time studies can be achieved, and apply these to the explosive Universe. Our main goal is to discover the physics that powers radio transients and compact-object mergers, by real-time detection of gravitational-wave and electromagnetic emission. Our previous work is world-leading in this field. We will now accelerate essential algorithms and pipelines, to understand the immediate energetics, composition and ejecta of these merging stars, for the first time. Our second goal is to understand how deep neural networks can be made more efficient, and how data-driven auto-tuned pipelines, on highly parallel accelerator hardware, can produce optimal results for all the sciences. To achieve these goals, we initiate a highly innovative program: CORTEX, the Center for Optimal, Real-Time Machine Studies of the Explosive Universe. CORTEX bridges fundamental research to society, and connects academic, applied, public and industry partners. Together we will provide the complex, real-time systems that are essential to, for the first time, reveal the exotic, dynamical Universe.