Related papers: The DAME/VO-Neural Infrastructure: an Integrated D…
We present the open source Astrophysical Multi-purpose Software Environment (AMUSE, www.amusecode.org), a component library for performing astrophysical simulations involving different physical domains and scales. It couples existing codes…
Understanding space weather is vital for the protection of our terrestrial and space infrastructure. In order to predict space weather accurately, large amounts of data are required, particularly in the extreme ultraviolet (EUV) spectrum.…
Astronomers are good at sharing data, but poorer at sharing knowledge. Almost all astronomical data ends up in open archives, and access to these is being simplified by the development of the global Virtual Observatory (VO). This is a great…
Distributed real-time and embedded (DRE) systems executing mixed criticality task sets are increasingly being deployed in mobile and embedded cloud computing platforms, including space applications. These DRE systems must not only operate…
This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific…
The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications…
We review the capabilities of the NASA/IPAC Extragalactic Database (NED, http://ned.ipac.caltech.edu) for information retrieval and knowledge discovery in the context of a globally distributed virtual observatory. Since its inception in…
In this document and the accompanying documents we describe a data model (Simulation Data Model) describing numerical computer simulations of astrophysical systems. The primary goal of this standard is to support discovery of simulations by…
The VESPA data access system focuses on applying Virtual Observatory (VO) standards and tools to Planetary Science. Building on a previous EC-funded Europlanet program, it has reached maturity during the first year of a new Europlanet 2020…
The Internet of Things (IoT) data and social media data are two of the fastest-growing data segments. Having high-quality data is crucial for making informed business decisions. The strategic process of leveraging insights from data is…
Deep learning models are increasingly data-hungry, requiring significant resources to collect and compile the datasets needed to train them, with Earth Observation (EO) models being no exception. However, the landscape of datasets in EO is…
The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often called a {\em Fourth Paradigm} for scientific research based on…
The challenges of multi-wavelength astrophysics require new outlooks from those appropriate to traditional astronomy. The next generation of research scientists must be trained to exploit the potentiality now being provided for the first…
Modern Earth Observation (EO) missions generate massive volumes of imagery that challenge existing downlink and ground-processing capabilities, particularly for time-critical applications. This work investigates how a low Earth orbit (LEO)…
IAU Inter-Commission B2-C1-C2 WG Data-driven Astronomy Education and Public Outreach (DAEPO) was launched officially in April 2017. With the development of many mega-science astronomical projects, for example CTA, DESI, EUCLID, FAST, GAIA,…
The site conditions that make astronomical observatories in space and on the ground so desirable -- cold and dark -- demand a physical remoteness that leads to limited data transmission capabilities. Such transmission limitations directly…
We shall present with examples how analysis of astronomy data can be used for an educational purpose to train students in methods of data analysis, statistics, programming skills and research problems. Special reference will be made to our…
AstroCloud is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of…
VO-Neural is the natural evolution of the Astroneural project which was started in 1994 with the aim to implement a suite of neural tools for data mining in astronomical massive data sets. At a difference with its ancestor, which was…
The era of data-intensive astronomy is being ushered in with the increasing size and complexity of observational data across wavelength and time domains, the development of algorithms to extract information from this complexity, and the…