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We report the outcomes of a survey that explores the current practices, needs and expectations of the astrophysics community, concerning four research aspects: open science practices, data access and management, data visualization, and data analysis. The survey, involving 329 professionals from several research institutions, pinpoints significant gaps in matters such as results reproducibility, availability of visual analytics tools and adoption of Machine Learning techniques for data analysis. This research is conducted in the context of the H2020 NEANIAS project.
We present a high-performance, graphics processing unit (GPU)-based framework for the efficient analysis and visualization of (nearly) terabyte (TB)-sized 3-dimensional images. Using a cluster of 96 GPUs, we demonstrate for a 0.5 TB image: (1) volume
Wide-angle surveys have been an engine for new discoveries throughout the modern history of astronomy, and have been among the most highly cited and scientifically productive observing facilities in recent years. This trend is likely to continue over
Astronomy began as a visual science, first through careful observations of the sky using either an eyepiece or the naked eye, then on to the preservation of those images with photographic media and finally the digital encoding of that information via
The U.S. Virtual Astronomical Observatory was a software infrastructure and development project designed both to begin the establishment of an operational Virtual Observatory (VO) and to provide the U.S. coordination with the international VO effort.
Upcoming and future astronomy research facilities will systematically generate terabyte-sized data sets moving astronomy into the Petascale data era. While such facilities will provide astronomers with unprecedented levels of accuracy and coverage, t