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.

The healthcare industry is under financial stress, faces increasing complexity of disease and co-morbidity, and is burdened by capacity constraints. Three major challenges have inhibited better use of health data: - data is not accessible and remains in silos; - data is not analysed to derive meaningful clinical insights; - insight isnt accessible for actioning by providers or patients to self/joint manage their condition. The EPI consortium aims to liberate, analyse, and action that data in a trustworthy way. This will empower patients and providers through self-management, shared management, and personalization across the full health spectrum.