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CRIS - Computational research infrastructure for science

TitleCRIS - Computational research infrastructure for science
Publication TypeConference Paper
Year of Publication2013
AuthorsDragut, EC, Baker, P, Xu, J, Sarfraz, MI, Bertino, E, Madhkour, A, Agarwal, R, Mahmood, A, Han, S
Secondary TitleInformation Reuse and Integration (IRI), 2013 IEEE 14th International Conference on
Date PublishedAug
Keywordsagronomy, automatic data quality monitoring, biochemistry, bioinformatics, CI, collaboration, collaborative scientific data management, Communities, computational expertise, computational research infrastructure for science, computational tools, Computer architecture, CRIS, cyberinfrastructure (CI), data dictionaries, data integration, data sharing, distributed databases, domain standards, domain vocabularies, Educational institutions, embedded provenance, heterogeneous data integration, local computer, provenance, remote computers, research activities, Scientific activity management, scientific community, scientific information systems, semantic definition, syntactic standard, Vocabulary, Workflow, workflow cyberinfrastructure

The challenges facing the scientific community are common and real: conduct relevant and verifiable research in a rapidly changing collaborative landscape with an ever increasing scale of data. It has come to a point where research activities cannot scale at the rate required without improved cyberinfrastructure (CI). In this paper we describe CRIS (The Computational Research Infrastructure for Science), with its primary tenets to provide an easy to use, scalable, and collaborative scientific data management and workflow cyberinfrastructure for scientists lacking extensive computational expertise. Some of the key features of CRIS are: 1) semantic definition of scientific data using domain vocabularies; 2) embedded provenance for all levels of research activity (data, workflows, tools etc.); 3) easy integration of existing heterogeneous data and computational tools on local or remote computers; 4) automatic data quality monitoring for syntactic and domain standards; and 5) shareable yet secure access to research data, computational tools and equipment. CRIS currently has a community of users in Agronomy, Biochemistry, Bioinformatics and Healthcare Engineering at Purdue University (cris.cyber.purdue.edu).

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