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SKA Science Data Challenge 2: analysis and results

Publications associées (37)

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Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data prin ...

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Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics

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Data resources are essential for the long-term preservation of scientific data and the reproducibility of science. The SIB Swiss Institute of Bioinformatics provides the life science community with a portfolio of openly accessible, high-quality databases a ...

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Scholarly Communication is still shifting from papers to richer content, as for instance research data and code. Data dissemination has a crucial role and yet it is in its infancy, with visible fragmentation and too many unknowns for researchers to get onb ...

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Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

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By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many ma ...

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Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We lin ...

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Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows

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Over the last two decades, the field of computational science has seen a dramatic shift towards incorporating high-throughput computation and big-data analysis as fundamental pillars of the scientific discovery process. This has necessitated the developmen ...

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