In the past Alexander S. Szalay has collaborated on articles with Randal Burns. One of their most recent publications is Chapter 7 - Scientific Data Federation: The World-Wide Telescope. Which was published in journal .

More information about Alexander S. Szalay research including statistics on their citations can be found on their Copernicus Academic profile page.

Alexander S. Szalay's Articles: (2)

Chapter 7 - Scientific Data Federation: The World-Wide Telescope

Publisher SummaryThis chapter deals with the revolutionary changes in astronomy resulting from advances in digital astronomy and the application of Grid concepts. The astronomy community is interested in mining the individual datasets looking for patterns, but there is even greater interest in cross-correlating the datasets to find new phenomena. The construction of astronomy archive involves massive calculations that ingest, analyze, and categorize the instrument data, producing databases and files. Each astronomy archive covers part of the electromagnetic spectrum for a period of time and a subset of the celestial sphere. All the archives are from the same sky and the same celestial objects, although different observations are made at different times. Increasingly, astronomers perform multispectral studies or temporal studies, combining data related to the same objects from multiple instruments and archives. Cross-comparison is possible because data are well documented and schematized with a common reference frame and have clear provenance. The Virtual Observatory (VO)—sometimes also called the World-Wide Telescope—is under construction in many countries, which seeks to provide portals, protocols, and standards that unify the world's astronomy archives into a giant database containing all astronomy literature, images, raw data, derived datasets, and simulation data-integrated as a single intelligent telescope.

NeuroViewFrom Cosmos to Connectomes: The Evolution of Data-Intensive Science

The analysis of data requires computation: originally by hand and more recently by computers. Different models of computing are designed and optimized for different kinds of data. In data-intensive science, the scale and complexity of data exceeds the comfort zone of local data stores on scientific workstations. Thus, cloud computing emerges as the preeminent model, utilizing data centers and high-performance clusters, enabling remote users to access and query subsets of the data efficiently. We examine how data-intensive computational systems originally built for cosmology, the Sloan Digital Sky Survey (SDSS), are now being used in connectomics, at the Open Connectome Project. We list lessons learned and outline the top challenges we expect to face. Success in computational connectomics would drastically reduce the time between idea and discovery, as SDSS did in cosmology.

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