Titlebook: Data-Intensive Radio Astronomy; Bringing Astrophysic Eleni Vardoulaki,Marta Dembska,Matthias Hoeft Book 2024 The Editor(s) (if applicable)

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Exploitation Platforms and Virtual Observatoryelivering radio astronomy data. Finally, the last section introduces the European initiative for Open Science in general, and the projects relevant to radio astronomy such as ESCAPE, that are foreseen to act as a platform for delivery of astronomy data and services, not only to the astronomy community but also to a broader audience.

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Epilogue and Outlookravelling. Finding ways to estimate and minimise the carbon footprint of radio astronomy has become a crucial and urgent priority for our community. As we explore the Universe and look to understand its past, we must not lose sight of the future, if we want to preserve both radio astronomy and our planet for future generations.

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Prediction and Tolerance Intervals, the context of data intensive radio astronomy (FITS, Casacore Measurement Set, ADIOS/HDF5) and a discussion on methods of lossy compression that can be applied to the data. In the final section of this chapter, we introduce the ways several instruments (LOFAR, Aperitif, MeerKAT, ASKAP, ALMA and MWA) ingest data into their archives.

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s a range of antenna and dish-based instruments through from single site facilities to large distributed radio arrays. We discuss each of the facilities in turn, highlighting the main challenges that drive the choices of their data processing and archiving implementations.

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Oxalacetic decarboxylase and related enzymes or datasets linking the radio source properties with other photometric and spectroscopic measurements, and informing further classification of the radio sources. It concludes with an overview of approaches being actively used in major projects with current generation leading radio telescope facilities.

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Friedrich H. Bruns,Hans Reinauerdesigned with the precise aim to solve new problems posed by, e.g. the SKA precursors and, eventually, the SKA telescope, such as the need to efficiently visualise multi-parameters data of very big size (e.g. terabyte scale) and possibly stored in a shared cloud where multiple users are working at the same time.

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https://doi.org/10.1007/978-3-476-03950-7my. Finally, it concludes with a retrospective look at past provenance implementations and the lessons that can be learnt from them with regards to scalability, understandability, and interoperability.

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Storage and Long Term Archivesribed. We then discuss what data lakes are or how they can be used in radio astronomy and the overlap between the data needs of astronomical archives and those of high-energy physics experiments. Finally, we give an overview of how the SKA data centres are planned.