scholarly journals EUDAT: A New Cross-Disciplinary Data Infrastructure for Science

2013 ◽  
Vol 8 (1) ◽  
pp. 279-287 ◽  
Author(s):  
Damien Lecarpentier ◽  
Peter Wittenburg ◽  
Willem Elbers ◽  
Alberto Michelini ◽  
Riam Kanso ◽  
...  

The EUDAT project is a pan-European data initiative that started in October 2011. The project brings together a unique consortium of 25 partners – including research communities, national data and high performance computing (HPC) centres, technology providers, and funding agencies – from 13 countries. EUDAT aims to build a sustainable cross-disciplinary and cross-national data infrastructure that provides a set of shared services for accessing and preserving research data.

Author(s):  
Mark G. Beckett ◽  
Chris R. Allton ◽  
Christine T.H. Davies ◽  
Ilan Davis ◽  
Jonathan M. Flynn ◽  
...  

We provide an insight into the challenge of building and supporting a scientific data infrastructure with reference to our experience working with scientists from computational particle physics and molecular biology. We illustrate how, with modern high-performance computing resources, even small scientific groups can generate huge volumes (petabytes) of valuable scientific data and explain how grid technology can be used to manage, publish, share and curate these data. We describe the D i GS software application, which we have developed to meet the needs of smaller communities and we have highlighted the key elements of its functionality.


2017 ◽  
Vol 17 (5) ◽  
pp. 81-88
Author(s):  
Dimitar Dimitrov ◽  
Emanouil Atanassov

Abstract The accounting platform is a web-service based system for collection and analysis of accounting data from different infrastructure resources like High Performance Computing (HPC), Cloud and storage systems. The platform has two major components - backend API services along with different data publishers and a client web UI module for visualization and operations. The backend API is designed to gather information from different job management systems, cloud vendors, and storage providers and use micro-service architecture. The web UI module is written in Python, JavaScript and has integrated SAML login module for user authentication and authorization. It is capable of visualizing the gathered data in dynamic OLAP style and supports standard export formats like CSV and Excel. Through the accounting platform, it is possible to obtain a full view of the usage patterns of an integrated electronic infrastructure and to see from one point all information about the different resources comprising the hybrid computing and data infrastructure.


MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.


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