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Data Sharing in CSCR: Towards In-Depth Long Term Collaboration
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Author(s): Christophe Reffay (Ecole Normale Supérieure de Cachan, France & Ecole Normale Supérieure de Lyon, France), Gregory Dyke (Carnegie Mellon University, USA)and Marie-Laure Betbeder (Université de Franche-Comté, France)
Copyright: 2012
Pages: 24
Source title:
Collaborative and Distributed E-Research: Innovations in Technologies, Strategies and Applications
Source Author(s)/Editor(s): Angel A. Juan (Open University of Catalonia, Spain), Thanasis Daradoumis (Open University of Catalonia, Spain), Meritxell Roca (Open University of Catalonia (UOC), Spain), Scott E. Grasman (Rochester Institute of Technology, USA)and J. Faulin (Public University of Navarre, Spain)
DOI: 10.4018/978-1-4666-0125-3.ch006
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Abstract
In this chapter, the authors show the importance of data in the research process and the potential benefit for communities to share research data. Although most of their references are taken from the fields of Computer Supported Collaborative Learning and Intelligent Tutoring Systems, they claim that their argument applies to any other field studying complex situations that need to be analyzed by different disciplines, methods, and instruments. The authors point out the evolution of scientific publication, especially its openness and the variety of its emerging forms. This leads them to propose corpora as boundary objects for various communities in the scientific sphere. Data release being itself a complex problem, the authors use the Mulce1 experience to show how sharable data can be built and made available. Once corpora are considered available, they discuss the potential of their reuse for multiple analyses or derivation. They focus on analytic representations and their combination with initial data or complementary analytic representations by presenting a tool named Tatiana. Finally, the authors propose their vision of data sharing in a world where scientists would use social network applications.
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